diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index bc8c217192..fe15fe0b63 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -100,10 +100,20 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       should-proceed: ${{ steps.check-pypi.outputs.success }}
+      status: ${{ steps.check-pypi.outputs.status }}
     steps:
       - name: Check PyPI package update
         id: check-pypi
         run: |
+          # Nightly publishing only runs in the canonical Langflow repository. Do not
+          # block fork PRs on the global PyPI package's daily upload schedule.
+          if [[ "${{ github.repository }}" != "langflow-ai/langflow" ]]; then
+            echo "Nightly PyPI check is not applicable to ${{ github.repository }}"
+            echo "success=true" >> $GITHUB_OUTPUT
+            echo "status=not-applicable" >> $GITHUB_OUTPUT
+            exit 0
+          fi
+
           # Get today's date in ISO format for comparison
           TODAY=$(date -u +"%Y-%m-%d")
           echo "Today's date: $TODAY"
@@ -118,6 +128,7 @@ jobs:
           if [ "$HTTP_STATUS" -ne 200 ]; then
             echo "Error: PyPI API returned HTTP status $HTTP_STATUS"
             echo "success=false" >> $GITHUB_OUTPUT
+            echo "status=unavailable" >> $GITHUB_OUTPUT
             exit 0
           fi
 
@@ -127,6 +138,7 @@ jobs:
             echo "Response preview:"
             head -n 10 response.json
             echo "success=false" >> $GITHUB_OUTPUT
+            echo "status=unavailable" >> $GITHUB_OUTPUT
             exit 0
           fi
 
@@ -136,6 +148,7 @@ jobs:
           if [ -z "$LATEST_VERSION" ]; then
             echo "Could not find a nightly (.devN) release"
             echo "success=false" >> $GITHUB_OUTPUT
+            echo "status=unavailable" >> $GITHUB_OUTPUT
             exit 0
           fi
 
@@ -145,6 +158,7 @@ jobs:
           if [ -z "$RELEASE_DATE" ]; then
             echo "Could not extract release date"
             echo "success=false" >> $GITHUB_OUTPUT
+            echo "status=unavailable" >> $GITHUB_OUTPUT
             exit 0
           fi
 
@@ -155,9 +169,11 @@ jobs:
           if [[ "$RELEASE_DATE" == "$TODAY" ]]; then
             echo "Package was updated today"
             echo "success=true" >> $GITHUB_OUTPUT
+            echo "status=healthy" >> $GITHUB_OUTPUT
           else
             echo "Package was not updated today"
             echo "success=false" >> $GITHUB_OUTPUT
+            echo "status=stale" >> $GITHUB_OUTPUT
           fi
 
           # Clean up
@@ -394,7 +410,7 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v7
         with:
-          ref: ${{ inputs.ref || github.ref }}
+          ref: ${{ inputs.ref || github.event.pull_request.head.sha || github.sha }}
 
       # SeaweedFS's S3 gateway: MinIO no longer publishes pullable images. A service
       # container cannot pass it the "server" command, so start it here. With no S3
@@ -557,7 +573,7 @@ jobs:
           echo "=== CI Status Summary ==="
           echo "Should run tests: ${{ needs.set-ci-condition.outputs.should-run-tests }}"
           echo "Should run CI: ${{ needs.set-ci-condition.outputs.should-run-ci }}"
-          echo "Nightly build status: ${{ needs.check-nightly-status.outputs.should-proceed }}"
+          echo "Nightly build status: ${{ needs.check-nightly-status.outputs.status }}"
           echo "Event type: ${{ github.event_name }}"
           echo "Docs only changes: $DOCS_ONLY"
           echo "Path filter decision: ${{ needs.path-filter.result }}"
@@ -620,9 +636,9 @@ jobs:
             echo "  2. Re-run the workflow if needed"
 
           elif [[ "${{ needs.check-nightly-status.outputs.should-proceed }}" != "true" && "${{ github.event_name }}" != "workflow_dispatch" && "$DOCS_ONLY" != "true" ]]; then
-            echo "🚫 CI BLOCKED: Nightly build is broken"
+            echo "🚫 CI BLOCKED: Nightly package is stale or unavailable"
             echo ""
-            echo "The nightly PyPI package was not updated today, indicating the nightly build failed."
+            echo "The latest nightly PyPI package was not updated today or its status could not be verified."
             echo ""
             echo "🔧 Next steps:"
             echo "  1. Work with the team to investigate and fix the nightly build"
@@ -637,7 +653,11 @@ jobs:
             echo "🎉 Your changes are ready:"
             echo "  - All tests passed"
             echo "  - Code quality checks passed"
-            echo "  - Nightly build is healthy"
+            if [[ "${{ needs.check-nightly-status.outputs.status }}" == "not-applicable" ]]; then
+              echo "  - Nightly status check is not applicable to this repository"
+            else
+              echo "  - Nightly build is healthy"
+            fi
           fi
 
           echo ""
diff --git a/.github/workflows/openxflow-production-release.yml b/.github/workflows/openxflow-production-release.yml
index bf95449819..08651dfa8a 100644
--- a/.github/workflows/openxflow-production-release.yml
+++ b/.github/workflows/openxflow-production-release.yml
@@ -2,7 +2,7 @@ name: OpenXFlow Production Release
 
 on:
   pull_request:
-    types: [closed]
+    types: [opened, synchronize, reopened, closed]
     branches: [main]
 
 permissions:
@@ -13,13 +13,167 @@ env:
   IMAGE_NAME: lien0219/openxflow
   PRODUCTION_REPOSITORY: lien0219/openxflow
 
-# Every merged PR gets its own run. Only the floating main promotion is
-# serialized, so a slow build cannot discard an immutable source image.
+# PR updates produce a verified artifact without registry publication. Every
+# merged PR gets its own release run; only floating main promotion is serialized.
 concurrency:
   group: openxflow-production-${{ github.event.pull_request.merge_commit_sha || github.run_id }}
   cancel-in-progress: false
 
 jobs:
+  preview-package:
+    name: Build and verify PR package
+    if: >-
+      github.repository == 'lien0219/openxflow' &&
+      github.event_name == 'pull_request' &&
+      github.event.action != 'closed' &&
+      github.event.pull_request.base.ref == 'main' &&
+      github.event.pull_request.base.repo.full_name == 'lien0219/openxflow'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    steps:
+      - name: Check out PR head
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event.pull_request.head.sha }}
+          fetch-depth: 1
+          persist-credentials: false
+
+      - name: Set up emulation and Buildx
+        uses: docker/setup-qemu-action@v3
+      - uses: docker/setup-buildx-action@v3
+
+      - name: Materialize the PR image verification plan
+        id: preview-plan
+        shell: bash
+        run: |
+          set -Eeuo pipefail
+          source="$(git rev-parse HEAD)"
+          version="$(cat deploy/IMAGE_VERSION)"
+          created="$(git show -s --format=%cI "$source")"
+          python - "$source" "$version" "$created" > /tmp/openxflow-pr-plan.json <<'PY'
+          import json
+          import sys
+          print(json.dumps({"source": sys.argv[1], "version": sys.argv[2], "created": sys.argv[3]}))
+          PY
+          {
+            echo "source=$source"
+            echo "version=$version"
+            echo "created=$created"
+          } >> "$GITHUB_OUTPUT"
+
+      - name: Build the PR OCI package
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: ./docker/build_and_push.Dockerfile
+          target: full
+          platforms: linux/amd64,linux/arm64
+          push: false
+          outputs: type=oci,dest=/tmp/openxflow-pr.oci.tar
+          build-args: |
+            OPENXFLOW_VERSION=${{ steps.preview-plan.outputs.version }}
+            OPENXFLOW_REVISION=${{ steps.preview-plan.outputs.source }}
+            OPENXFLOW_CREATED=${{ steps.preview-plan.outputs.created }}
+            OPENXFLOW_SOURCE=https://github.com/lien0219/openxflow
+            OPENXFLOW_URL=https://github.com/lien0219/openxflow
+          labels: |
+            org.opencontainers.image.title=OpenXFlow
+            org.opencontainers.image.description=OpenXFlow workflow automation platform
+            org.opencontainers.image.version=${{ steps.preview-plan.outputs.version }}
+            org.opencontainers.image.revision=${{ steps.preview-plan.outputs.source }}
+            org.opencontainers.image.created=${{ steps.preview-plan.outputs.created }}
+            org.opencontainers.image.source=https://github.com/lien0219/openxflow
+            org.opencontainers.image.url=https://github.com/lien0219/openxflow
+          cache-from: type=gha,scope=openxflow-pr-${{ github.event.pull_request.number }}
+          cache-to: type=gha,mode=max,scope=openxflow-pr-${{ github.event.pull_request.number }}
+          provenance: mode=max
+          sbom: true
+
+      # Trivy's --input accepts Docker save tarballs or an unpacked OCI layout,
+      # not a Buildx type=oci tar archive. Convert each platform separately so
+      # both architectures receive the same blocking vulnerability/secret scan.
+      - name: Prepare per-architecture Trivy scan inputs (PR)
+        shell: bash
+        run: |
+          set -Eeuo pipefail
+          sudo apt-get update -qq
+          sudo apt-get install -y -qq skopeo jq
+          for arch in amd64 arm64; do
+            output="/tmp/openxflow-pr-${arch}.docker.tar"
+            skopeo copy --override-os linux --override-arch "$arch" \
+              "oci-archive:/tmp/openxflow-pr.oci.tar" \
+              "docker-archive:${output}:openxflow-scan:${arch}"
+            actual="$(skopeo inspect "docker-archive:${output}" | jq -r '.Architecture // empty')"
+            if [ "$actual" != "$arch" ]; then
+              echo "::error::Expected $arch scan input, got ${actual:-missing}"
+              exit 1
+            fi
+          done
+
+      - name: Prepare Trivy evidence folder (PR)
+        shell: bash
+        run: mkdir -p /tmp/openxflow-trivy
+
+      - name: Scan the unpushed PR amd64 image
+        uses: aquasecurity/trivy-action@ed142fd0673e97e23eac54620cfb913e5ce36c25 # v0.36.0
+        with:
+          input: /tmp/openxflow-pr-amd64.docker.tar
+          format: json
+          output: /tmp/openxflow-trivy/pr-amd64.json
+          exit-code: "1"
+          ignore-unfixed: "false"
+          severity: CRITICAL,HIGH
+          scanners: vuln,secret
+          trivyignores: .trivyignore.yaml
+
+      - name: Scan the unpushed PR arm64 image
+        uses: aquasecurity/trivy-action@ed142fd0673e97e23eac54620cfb913e5ce36c25 # v0.36.0
+        with:
+          input: /tmp/openxflow-pr-arm64.docker.tar
+          format: json
+          output: /tmp/openxflow-trivy/pr-arm64.json
+          exit-code: "1"
+          ignore-unfixed: "false"
+          severity: CRITICAL,HIGH
+          scanners: vuln,secret
+          trivyignores: .trivyignore.yaml
+
+      - name: Verify PR package attestations and runtime probes
+        shell: bash
+        run: |
+          set -Eeuo pipefail
+          python scripts/ci/openxflow_verify_image.py \
+            --archive /tmp/openxflow-pr.oci.tar \
+            --plan /tmp/openxflow-pr-plan.json \
+            --output-dir /tmp/openxflow-pr-image-verification
+
+      - name: Retain failed and successful Trivy PR reports
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: openxflow-pr-trivy-${{ github.event.pull_request.number }}-${{ steps.preview-plan.outputs.source }}
+          path: /tmp/openxflow-trivy/
+          if-no-files-found: ignore
+          retention-days: 14
+
+      - name: Retain PR image verification evidence
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: openxflow-pr-image-verification-${{ github.event.pull_request.number }}-${{ steps.preview-plan.outputs.source }}
+          path: /tmp/openxflow-pr-image-verification
+          if-no-files-found: ignore
+          retention-days: 7
+
+      - name: Upload verified PR OCI package
+        uses: actions/upload-artifact@v4
+        with:
+          name: openxflow-pr-oci-${{ github.event.pull_request.number }}-${{ steps.preview-plan.outputs.source }}
+          path: /tmp/openxflow-pr.oci.tar
+          if-no-files-found: error
+          retention-days: 7
+
   validate-merge:
     name: Validate exact merged main source
     if: >-
@@ -297,15 +451,63 @@ jobs:
             'import importlib.util; assert importlib.util.find_spec("langflow"); assert importlib.util.find_spec("pgvector")'
           docker run --rm --entrypoint langflow openxflow-runtime-probe:local --help >/dev/null
 
-      - name: Scan the unpushed OCI archive (or recovered retry)
-        uses: aquasecurity/trivy-action@0.28.0
+      # Trivy's --input accepts Docker save tarballs or an unpacked OCI layout,
+      # not a Buildx type=oci tar archive. Convert each platform separately so
+      # both architectures receive the same blocking vulnerability/secret scan.
+      - name: Prepare per-architecture Trivy scan inputs (production)
+        shell: bash
+        run: |
+          set -Eeuo pipefail
+          sudo apt-get update -qq
+          sudo apt-get install -y -qq skopeo jq
+          for arch in amd64 arm64; do
+            output="/tmp/openxflow-${arch}.docker.tar"
+            skopeo copy --override-os linux --override-arch "$arch" \
+              "oci-archive:/tmp/openxflow.oci.tar" \
+              "docker-archive:${output}:openxflow-scan:${arch}"
+            actual="$(skopeo inspect "docker-archive:${output}" | jq -r '.Architecture // empty')"
+            if [ "$actual" != "$arch" ]; then
+              echo "::error::Expected $arch scan input, got ${actual:-missing}"
+              exit 1
+            fi
+          done
+
+      - name: Prepare Trivy evidence folder (production)
+        shell: bash
+        run: mkdir -p /tmp/openxflow-trivy
+
+      - name: Scan the unpushed production amd64 image
+        uses: aquasecurity/trivy-action@ed142fd0673e97e23eac54620cfb913e5ce36c25 # v0.36.0
         with:
-          input: /tmp/openxflow.oci.tar
-          format: table
+          input: /tmp/openxflow-amd64.docker.tar
+          format: json
+          output: /tmp/openxflow-trivy/production-amd64.json
           exit-code: "1"
           ignore-unfixed: "false"
           severity: CRITICAL,HIGH
           scanners: vuln,secret
+          trivyignores: .trivyignore.yaml
+
+      - name: Scan the unpushed production arm64 image
+        uses: aquasecurity/trivy-action@ed142fd0673e97e23eac54620cfb913e5ce36c25 # v0.36.0
+        with:
+          input: /tmp/openxflow-arm64.docker.tar
+          format: json
+          output: /tmp/openxflow-trivy/production-arm64.json
+          exit-code: "1"
+          ignore-unfixed: "false"
+          severity: CRITICAL,HIGH
+          scanners: vuln,secret
+          trivyignores: .trivyignore.yaml
+
+      - name: Retain production Trivy reports
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: openxflow-production-trivy-${{ github.run_id }}-${{ github.run_attempt }}
+          path: /tmp/openxflow-trivy/
+          if-no-files-found: ignore
+          retention-days: 14
 
       - name: Verify OCI attestations, both architectures, and runtime probes
         shell: bash
diff --git a/.github/workflows/openxflow-security-lock-refresh.yml b/.github/workflows/openxflow-security-lock-refresh.yml
new file mode 100644
index 0000000000..29d140ab3d
--- /dev/null
+++ b/.github/workflows/openxflow-security-lock-refresh.yml
@@ -0,0 +1,82 @@
+name: OpenXFlow Security Lockfile Refresh Evidence
+
+on:
+  push:
+    branches: [develop]
+    paths:
+      - pyproject.toml
+      - docker/patch_hardened_pip_vendor.sh
+      - .github/workflows/openxflow-security-lock-refresh.yml
+
+permissions:
+  contents: write
+
+jobs:
+  regenerate-lock:
+    runs-on: ubuntu-24.04
+    timeout-minutes: 35
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.10.4"
+          enable-cache: true
+      - name: Re-lock four patched security dependencies
+        run: |
+          set -euo pipefail
+          uv lock --upgrade-package authlib --upgrade-package dnspython --upgrade-package msgpack --upgrade-package pymongo
+          uv lock --check
+          python - <<'PY'
+          import tomllib
+          from packaging.version import Version
+          with open("uv.lock", "rb") as f:
+              locked = tomllib.load(f)
+          versions = {}
+          for package in locked["package"]:
+              versions.setdefault(package["name"], set()).add(Version(package["version"]))
+          floors = {"authlib": "1.9.1", "msgpack": "1.2.3", "pymongo": "4.18.3"}
+          for pkg, floor in floors.items():
+              assert versions[pkg] and min(versions[pkg]) >= Version(floor), (pkg, versions[pkg])
+              print(f"{pkg}={sorted(versions[pkg])} >= {floor}")
+          assert Version("2.9.0") in versions["dnspython"], versions["dnspython"]
+          print("dnspython resolves >=2.9.0 for the production Python 3.14 environment")
+          PY
+      - name: Commit resolver-generated lockfile to develop
+        run: |
+          set -euo pipefail
+          git diff --exit-code -- uv.lock && exit 0
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          git add uv.lock
+          git commit -m "fix(deps): resolve patched security dependencies in uv.lock"
+          git push origin "HEAD:develop"
+      - name: Verify architecture-specific pip vendor wheel hashes from PyPI
+        run: |
+          python - <<'PY'
+          import json
+          import pathlib
+          import re
+          import urllib.request
+
+          data = json.load(urllib.request.urlopen("https://pypi.org/pypi/msgpack/1.2.3/json", timeout=30))
+          script = pathlib.Path("docker/patch_hardened_pip_vendor.sh").read_text()
+          matches = re.findall(r'msgpack_hash="([a-f0-9]{64})"', script)
+          assert len(matches) == 2
+          for arch, expected in zip(("x86_64", "aarch64"), matches):
+              wheels = [
+                  item for item in data["urls"]
+                  if "-cp314-cp314-" in item["filename"]
+                  and ("manylinux2014_" + arch) in item["filename"]
+              ]
+              assert len(wheels) == 1, (arch, [v["filename"] for v in wheels])
+              actual = wheels[0]["digests"]["sha256"]
+              assert actual == expected, (arch, "pinned SHA256 mismatch", actual, expected)
+              print(f"{arch}: PyPI SHA256 verified")
+          PY
+      - name: Upload resolver-generated lockfile
+        uses: actions/upload-artifact@v4
+        with:
+          name: openxflow-patched-uv-lock
+          path: uv.lock
+          if-no-files-found: error
+          retention-days: 7
diff --git a/.trivyignore.yaml b/.trivyignore.yaml
new file mode 100644
index 0000000000..c23f4e914f
--- /dev/null
+++ b/.trivyignore.yaml
@@ -0,0 +1,12 @@
+# Exception for a third-party SDK's literal property-name example, not a credential.
+# Review with IBM Watson SDK upgrades; expires automatically.
+secrets:
+  - id: gcp-service-account
+    paths:
+      - "**/site-packages/ibm_watsonx_ai/connections.py"
+      - "**/site-packages/ibm_watsonx_ai/__pycache__/connections.cpython-314.pyc"
+    expired_at: 2026-12-31
+    statement: >-
+      IBM Watson SDK example property mapping includes the literal identifier
+      gcp_service_account; no service-account JSON private key is embedded.
+      Only this rule in this SDK source file and its deterministic CPython 3.14\n      compiled bytecode are excluded; all other findings still block.
diff --git a/docker/build_and_push.Dockerfile b/docker/build_and_push.Dockerfile
index 87cbfdd220..fa96129bd6 100644
--- a/docker/build_and_push.Dockerfile
+++ b/docker/build_and_push.Dockerfile
@@ -5,7 +5,7 @@
 # so the base and full targets cannot silently resolve different build tools.
 ARG UV_VERSION=0.10.4
 ARG PYTHON_IMAGE=registry.access.redhat.com/ubi10/python-314-minimal@sha256:3754d6dacac3177e62637420cadf7dcf438e390f664c527b0457a79653a47488
-ARG NODE_VERSION=24.21.0
+ARG NODE_VERSION=24.22.1
 
 FROM ghcr.io/astral-sh/uv:${UV_VERSION} AS uv-installer
 
@@ -41,8 +41,8 @@ RUN microdnf install -y \
        elif [ "$ARCH" = "aarch64" ]; then NODE_ARCH="arm64"; \
        else echo "Unsupported Node architecture: $ARCH" >&2; exit 1; fi \
     && case "${NODE_VERSION}:${NODE_ARCH}" in \
-        24.21.0:x64) NODE_SHA256=fd8e59d5a511510f6a298afb548f18c7d2b1be404d8b4a27d94fbe49f56cb2d6 ;; \
-        24.21.0:arm64) NODE_SHA256=6ad1325edbdb5649c379b75a237147a666c95d4f9ae8d340fef2d1575d289ad2 ;; \
+        24.22.1:x64) NODE_SHA256=6c172a9ab6718f7b801fb5a7bd486af2ee5e32758086a04b84fcce2aa77095e6 ;; \
+        24.22.1:arm64) NODE_SHA256=dbabf7a1cbf947e6059ffad2c27b80041db97dea1e553b62afdee7f13fca336e ;; \
         *) echo "No reviewed Node checksum for ${NODE_VERSION}:${NODE_ARCH}" >&2; exit 1 ;; \
        esac \
     && curl -fsSLo /tmp/node.tar.xz \
@@ -208,6 +208,9 @@ RUN microdnf update -y \
     && microdnf clean all
 
 RUN python3.14 -m pip install --no-cache-dir --upgrade "pip==26.2.1"
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh
 
 COPY --from=builder-base /usr/local/bin/uv /usr/local/bin/uv
 COPY --from=builder-base /usr/local/bin/uvx /usr/local/bin/uvx
@@ -258,6 +261,10 @@ ENV PATH="/app/.venv/bin:$PATH" \
 # Keep auto-login disabled in every public target through the shared runtime stage.
 ENV LANGFLOW_AUTO_LOGIN=false
 
+# Publish the declared HTTP port in the image configuration for every target
+# inheriting this runtime stage. Listening still uses LANGFLOW_HOST/PORT above.
+EXPOSE 7860/tcp
+
 USER user
 WORKDIR /app
 
@@ -270,8 +277,14 @@ CMD ["langflow", "run"]
 FROM runtime AS base
 
 COPY --from=base-builder --chown=1000:0 /app/.venv /app/.venv
+USER root
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh \
+    && find /app/.venv -type d -path '*/pip/_vendor' -exec chown -R 1000:0 {} +
 LABEL org.opencontainers.image.title="OpenXFlow" \
       com.openxflow.image.profile="base"
+USER user
 
 ################################
 # FULL-BUNDLES IMAGE
@@ -280,8 +293,14 @@ LABEL org.opencontainers.image.title="OpenXFlow" \
 FROM runtime AS full-bundles
 
 COPY --from=full-bundles-builder --chown=1000:0 /app/.venv /app/.venv
+USER root
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh \
+    && find /app/.venv -type d -path '*/pip/_vendor' -exec chown -R 1000:0 {} +
 LABEL org.opencontainers.image.title="OpenXFlow" \
       com.openxflow.image.profile="full-bundles"
+USER user
 
 ################################
 # DEFAULT IMAGE (default/final target)
@@ -290,5 +309,11 @@ LABEL org.opencontainers.image.title="OpenXFlow" \
 FROM runtime AS full
 
 COPY --from=full-builder --chown=1000:0 /app/.venv /app/.venv
+USER root
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh \
+    && find /app/.venv -type d -path '*/pip/_vendor' -exec chown -R 1000:0 {} +
 LABEL org.opencontainers.image.title="OpenXFlow" \
       com.openxflow.image.profile="full"
+USER user
diff --git a/docker/build_and_push_backend.Dockerfile b/docker/build_and_push_backend.Dockerfile
index 70ff05f412..67c92f0e5f 100644
--- a/docker/build_and_push_backend.Dockerfile
+++ b/docker/build_and_push_backend.Dockerfile
@@ -7,7 +7,7 @@
 
 ARG UV_VERSION=0.10.4
 ARG PYTHON_IMAGE=registry.access.redhat.com/ubi10/python-314-minimal
-ARG NODE_VERSION=24.21.0
+ARG NODE_VERSION=24.22.1
 
 ################################
 # BUILDER
@@ -69,6 +69,9 @@ RUN microdnf update -y \
         xz tar \
     && microdnf clean all
 RUN python3.14 -m pip install --no-cache-dir --upgrade "pip==26.2.1"
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh
 COPY --from=builder /usr/local/bin/uv /usr/local/bin/uv
 COPY --from=builder /usr/local/bin/uvx /usr/local/bin/uvx
 # Install Node.js (required for npx-based MCP stdio servers).
@@ -82,8 +85,16 @@ RUN ARCH=$(uname -m) \
     && if [ "$ARCH" = "x86_64" ]; then NODE_ARCH="x64"; \
        elif [ "$ARCH" = "aarch64" ]; then NODE_ARCH="arm64"; \
        else NODE_ARCH="$ARCH"; fi \
-    && curl -fsSL "https://nodejs.org/dist/v${NODE_VERSION}/node-v${NODE_VERSION}-linux-${NODE_ARCH}.tar.xz" \
-    | tar -xJ -C /usr/local --strip-components=1 \
+    && case "${NODE_VERSION}:${NODE_ARCH}" in \
+        24.22.1:x64) NODE_SHA256=6c172a9ab6718f7b801fb5a7bd486af2ee5e32758086a04b84fcce2aa77095e6 ;; \
+        24.22.1:arm64) NODE_SHA256=dbabf7a1cbf947e6059ffad2c27b80041db97dea1e553b62afdee7f13fca336e ;; \
+        *) echo "No reviewed Node checksum for ${NODE_VERSION}:${NODE_ARCH}" >&2; exit 1 ;; \
+       esac \
+    && curl -fsSLo /tmp/node.tar.xz \
+        "https://nodejs.org/dist/v${NODE_VERSION}/node-v${NODE_VERSION}-linux-${NODE_ARCH}.tar.xz" \
+    && printf '%s  /tmp/node.tar.xz\n' "$NODE_SHA256" | sha256sum --check --strict \
+    && tar -xJf /tmp/node.tar.xz -C /usr/local --strip-components=1 \
+    && rm -f /tmp/node.tar.xz \
     && sh /tmp/install_hardened_npm.sh \
     && rm -f /tmp/install_hardened_npm.sh
 
@@ -92,6 +103,10 @@ RUN useradd --uid 1000 --gid 0 --no-create-home --home-dir /app/data user
 
 # Copy only the virtual environment
 COPY --from=builder --chown=1000:0 /app/.venv /app/.venv
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh \
+    && find /app/.venv -type d -path '*/pip/_vendor' -exec chown -R 1000:0 {} +
 ENV PATH="/app/.venv/bin:$PATH"
 ENV HOME=/app/data
 ENV BASH_ENV="" \
diff --git a/docker/build_and_push_ep.Dockerfile b/docker/build_and_push_ep.Dockerfile
index 379a915b14..12e140fada 100644
--- a/docker/build_and_push_ep.Dockerfile
+++ b/docker/build_and_push_ep.Dockerfile
@@ -3,7 +3,7 @@
 
 ARG UV_VERSION=0.10.4
 ARG PYTHON_IMAGE=registry.access.redhat.com/ubi10/python-314-minimal
-ARG NODE_VERSION=24.21.0
+ARG NODE_VERSION=24.22.1
 
 ################################
 # BUILDER-BASE
@@ -117,6 +117,9 @@ RUN microdnf update -y \
     && microdnf install -y curl git libpq gnupg xz tar shadow-utils \
     && microdnf clean all
 RUN python3.14 -m pip install --no-cache-dir --upgrade "pip==26.2.1"
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh
 COPY --from=builder /usr/local/bin/uv /usr/local/bin/uv
 COPY --from=builder /usr/local/bin/uvx /usr/local/bin/uvx
 # NODE_VERSION and the npm major below are coupled: npm 12 requires Node
@@ -129,13 +132,25 @@ RUN ARCH=$(uname -m) \
     && if [ "$ARCH" = "x86_64" ]; then NODE_ARCH="x64"; \
        elif [ "$ARCH" = "aarch64" ]; then NODE_ARCH="arm64"; \
        else NODE_ARCH="$ARCH"; fi \
-    && curl -fsSL "https://nodejs.org/dist/v${NODE_VERSION}/node-v${NODE_VERSION}-linux-${NODE_ARCH}.tar.xz" \
-    | tar -xJ -C /usr/local --strip-components=1 \
+    && case "${NODE_VERSION}:${NODE_ARCH}" in \
+        24.22.1:x64) NODE_SHA256=6c172a9ab6718f7b801fb5a7bd486af2ee5e32758086a04b84fcce2aa77095e6 ;; \
+        24.22.1:arm64) NODE_SHA256=dbabf7a1cbf947e6059ffad2c27b80041db97dea1e553b62afdee7f13fca336e ;; \
+        *) echo "No reviewed Node checksum for ${NODE_VERSION}:${NODE_ARCH}" >&2; exit 1 ;; \
+       esac \
+    && curl -fsSLo /tmp/node.tar.xz \
+        "https://nodejs.org/dist/v${NODE_VERSION}/node-v${NODE_VERSION}-linux-${NODE_ARCH}.tar.xz" \
+    && printf '%s  /tmp/node.tar.xz\n' "$NODE_SHA256" | sha256sum --check --strict \
+    && tar -xJf /tmp/node.tar.xz -C /usr/local --strip-components=1 \
+    && rm -f /tmp/node.tar.xz \
     && sh /tmp/install_hardened_npm.sh \
     && rm -f /tmp/install_hardened_npm.sh
 RUN useradd user -u 1000 -g 0 --no-create-home --home-dir /app/data
 
 COPY --from=builder --chown=1000 /app/.venv /app/.venv
+COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh
+RUN sh /tmp/patch_hardened_pip_vendor.sh \
+    && rm -f /tmp/patch_hardened_pip_vendor.sh \
+    && find /app/.venv -type d -path '*/pip/_vendor' -exec chown -R 1000:0 {} +
 ENV PATH="/app/.venv/bin:$PATH"
 ENV HOME=/app/data
 ENV BASH_ENV="" \
diff --git a/docker/install_hardened_npm.sh b/docker/install_hardened_npm.sh
index 7249e869d6..d72976ce99 100644
--- a/docker/install_hardened_npm.sh
+++ b/docker/install_hardened_npm.sh
@@ -2,11 +2,12 @@
 
 set -eu
 
-NPM_VERSION="12.0.2"
+NPM_VERSION="12.2.1"
 IP_ADDRESS_VERSION="10.7.1"
 BRACE_EXPANSION_VERSION="5.0.12"
 TAR_VERSION="7.5.22"
-UNDICI_VERSION="6.28.1"
+UNDICI_VERSION="6.29.1"
+HTTP_CACHE_SEMANTICS_VERSION="4.3.0"
 npm_cache="$(npm config get cache)"
 
 case "$npm_cache" in
@@ -38,9 +39,10 @@ npm install --prefix "$patch_root" \
     "ip-address@${IP_ADDRESS_VERSION}" \
     "brace-expansion@${BRACE_EXPANSION_VERSION}" \
     "tar@${TAR_VERSION}" \
-    "undici@${UNDICI_VERSION}"
+    "undici@${UNDICI_VERSION}" \
+    "http-cache-semantics@${HTTP_CACHE_SEMANTICS_VERSION}"
 
-for package in ip-address brace-expansion tar undici; do
+for package in ip-address brace-expansion tar undici http-cache-semantics; do
     rm -rf "$npm_root/$package"
     cp -a "$patch_root/node_modules/$package" "$npm_root/$package"
 done
@@ -51,7 +53,8 @@ const expected = {
   "ip-address": "10.7.1",
   "brace-expansion": "5.0.12",
   tar: "7.5.22",
-  undici: "6.28.1",
+  undici: "6.29.1",
+  "http-cache-semantics": "4.3.0",
   sigstore: "5.0.0",
   "@sigstore/core": "4.0.1",
   "tinyglobby/node_modules/picomatch": "4.0.5",
diff --git a/docker/patch_hardened_pip_vendor.sh b/docker/patch_hardened_pip_vendor.sh
new file mode 100644
index 0000000000..34f6640635
--- /dev/null
+++ b/docker/patch_hardened_pip_vendor.sh
@@ -0,0 +1,225 @@
+#!/bin/sh
+
+set -eu
+
+python_bin="${PYTHON_BIN:-python3.14}"
+stage_dir="$(mktemp -d)"
+trap 'rm -rf "$stage_dir"' EXIT
+
+case "$(uname -m)" in
+    x86_64)
+        msgpack_hash="d8ef3a66e4b52d2d7fdd90df2984670124b2ff7546d76bb25dcf68ef47f7df58" # pragma: allowlist secret -- public wheel integrity hash
+        ;;
+    aarch64)
+        msgpack_hash="9324c54995641c3d1f92a9d55093c8cde0ffa2fbc87a467a688ef60428393220" # pragma: allowlist secret -- public wheel integrity hash
+        ;;
+    *)
+        echo "Unsupported architecture for pinned msgpack wheel: $(uname -m)" >&2
+        exit 1
+        ;;
+esac
+
+cat > "$stage_dir/requirements.txt" <<EOF
+certifi==2026.10.23 --hash=sha256:e9b54de76e530bb6cf0ccd036787103acb58fd42ba930ea8bf948ddbaa2f61d1
+msgpack==1.2.3 --hash=sha256:${msgpack_hash}
+setuptools==78.1.1 --hash=sha256:c3a9c4211ff4c309edb8b8c4f1cbfa7ae324c4ba9f91ff254e3d305b9fd54561
+urllib3==2.8.0 --hash=sha256:0cf3cae568d36aa9576b28dfb35f11328f1cb974ca7647d9475ebb86c75ac6e3
+EOF
+
+"$python_bin" -m pip install \
+    --disable-pip-version-check \
+    --no-cache-dir \
+    --no-deps \
+    --only-binary=:all: \
+    --require-hashes \
+    --target "$stage_dir" \
+    -r "$stage_dir/requirements.txt"
+
+STAGE_DIR="$stage_dir" "$python_bin" - <<'PY'
+import json
+import os
+import re
+import shutil
+from pathlib import Path
+
+stage = Path(os.environ["STAGE_DIR"])
+certifi_core = stage / "certifi" / "core.py"
+source = certifi_core.read_text(encoding="utf-8")
+for old, new in (
+    ('files("certifi")', 'files("pip._vendor.certifi")'),
+    ('get_path("certifi",', 'get_path("pip._vendor.certifi",'),
+    ('read_text("certifi",', 'read_text("pip._vendor.certifi",'),
+):
+    if old not in source:
+        raise SystemExit(f"unexpected certifi import-resource expression: {old}")
+    source = source.replace(old, new)
+certifi_core.write_text(source, encoding="utf-8")
+
+urllib3_request = stage / "urllib3" / "util" / "request.py"
+source = urllib3_request.read_text(encoding="utf-8")
+brotli_block = re.compile(
+    r"\ntry:\n"
+    r"    try:\n"
+    r"        import brotlicffi as _unused_module_brotli.*?\n"
+    r"    except ImportError:\n"
+    r"        import brotli as _unused_module_brotli.*?\n"
+    r"except ImportError:\n"
+    r"    pass\n"
+    r"else:\n"
+    r"    ACCEPT_ENCODING \+= \",br\"\n",
+    flags=re.DOTALL,
+)
+source, count = brotli_block.subn("\n", source, count=1)
+if count != 1:
+    raise SystemExit("unexpected urllib3 brotli vendoring block")
+urllib3_request.write_text(source, encoding="utf-8")
+
+pkg_resources = stage / "pkg_resources" / "__init__.py"
+source = pkg_resources.read_text(encoding="utf-8")
+
+packaging_imports = """import packaging.markers
+import packaging.requirements
+import packaging.specifiers
+import packaging.utils
+import packaging.version
+"""
+patched_packaging_imports = """import pip._vendor.packaging as packaging
+import pip._vendor.packaging.markers
+import pip._vendor.packaging.requirements
+import pip._vendor.packaging.specifiers
+import pip._vendor.packaging.utils
+import pip._vendor.packaging.version
+"""
+if packaging_imports not in source:
+    raise SystemExit("unexpected setuptools pkg_resources packaging imports")
+source = source.replace(packaging_imports, patched_packaging_imports, 1)
+
+jaraco_import = "from jaraco.text import drop_comment, join_continuation, yield_lines"
+patched_jaraco_import = "from pip._internal.utils._jaraco_text import drop_comment, join_continuation, yield_lines"
+if jaraco_import not in source:
+    raise SystemExit("unexpected setuptools pkg_resources jaraco import")
+source = source.replace(jaraco_import, patched_jaraco_import, 1)
+
+platformdirs_import = "from platformdirs import user_cache_dir as _user_cache_dir"
+patched_platformdirs_import = "from pip._vendor.platformdirs import user_cache_dir as _user_cache_dir"
+if platformdirs_import not in source:
+    raise SystemExit("unexpected setuptools pkg_resources platformdirs import")
+source = source.replace(platformdirs_import, patched_platformdirs_import, 1)
+
+deprecation_warning = '''warnings.warn(
+    "pkg_resources is deprecated as an API. "
+    "See https://setuptools.pypa.io/en/latest/pkg_resources.html",
+    DeprecationWarning,
+    stacklevel=2,
+)
+'''
+if deprecation_warning not in source:
+    raise SystemExit("unexpected setuptools pkg_resources warning")
+source = source.replace(
+    deprecation_warning,
+    "# pip vendors pkg_resources for its Python <3.11 metadata backend.\n",
+    1,
+)
+pkg_resources.write_text(source, encoding="utf-8")
+
+roots = sorted(
+    {
+        path.parent
+        for parent in (Path("/app"), Path("/opt/app-root"))
+        if parent.exists()
+        for path in parent.glob("**/pip/_vendor/vendor.txt")
+    }
+)
+if not roots:
+    raise SystemExit("no pip vendor roots found")
+
+versions = {
+    "certifi": "2026.10.23",
+    "msgpack": "1.2.3",
+    "setuptools": "78.1.1",
+    "urllib3": "2.8.0",
+}
+for root in roots:
+    for package in ("certifi", "msgpack", "urllib3", "pkg_resources"):
+        destination = root / package
+        if destination.exists() or destination.is_symlink():
+            if destination.is_dir() and not destination.is_symlink():
+                shutil.rmtree(destination)
+            else:
+                destination.unlink()
+        ignore = shutil.ignore_patterns("tests", "api_tests.txt") if package == "pkg_resources" else None
+        shutil.copytree(stage / package, destination, ignore=ignore)
+        if package == "pkg_resources":
+            assert not (destination / "tests").exists()
+            assert not (destination / "api_tests.txt").exists()
+
+    vendor_txt = root / "vendor.txt"
+    lines = vendor_txt.read_text(encoding="utf-8").splitlines()
+    replacements = {
+        "certifi": f"certifi=={versions['certifi']}",
+        "msgpack": f"msgpack=={versions['msgpack']}",
+        "setuptools": f"setuptools=={versions['setuptools']}",
+        "urllib3": f"urllib3=={versions['urllib3']}",
+    }
+    seen = set()
+    for index, line in enumerate(lines):
+        name = line.strip().split("==", 1)[0]
+        if name in replacements:
+            indent = line[: len(line) - len(line.lstrip())]
+            lines[index] = indent + replacements[name]
+            seen.add(name)
+    if seen != set(replacements):
+        raise SystemExit(f"vendor.txt package list mismatch in {vendor_txt}: {seen}")
+    vendor_txt.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+    bom_path = root / "bom.cdx.json"
+    bom = json.loads(bom_path.read_text(encoding="utf-8"))
+    refs = {}
+    for component in bom.get("components", []):
+        name = component.get("name")
+        if name in versions:
+            old_ref = component.get("bom-ref")
+            new_ref = f"pkg:pypi/{name}@{versions[name]}"
+            if old_ref:
+                refs[old_ref] = new_ref
+            component["bom-ref"] = new_ref
+            component["purl"] = new_ref
+            component["version"] = versions[name]
+    if len(refs) != len(versions):
+        raise SystemExit(f"BOM package list mismatch in {bom_path}: {refs}")
+
+    def replace_refs(value):
+        if isinstance(value, dict):
+            return {key: replace_refs(item) for key, item in value.items()}
+        if isinstance(value, list):
+            return [replace_refs(item) for item in value]
+        return refs.get(value, value)
+
+    bom_path.write_text(json.dumps(replace_refs(bom), indent=2) + "\n", encoding="utf-8")
+
+    certifi_source = (root / "certifi" / "core.py").read_text(encoding="utf-8")
+    assert 'files("pip._vendor.certifi")' in certifi_source
+    assert 'get_path("pip._vendor.certifi",' in certifi_source
+    assert 'read_text("pip._vendor.certifi",' in certifi_source
+    urllib3_source = (root / "urllib3" / "util" / "request.py").read_text(encoding="utf-8")
+    assert ",br" not in urllib3_source
+    assert not (root / "pkg_resources" / "tests").exists()
+    assert not (root / "pkg_resources" / "api_tests.txt").exists()
+PY
+
+"$python_bin" - <<'PY'
+from pathlib import Path
+
+from pip._vendor import certifi, msgpack, pkg_resources, urllib3
+
+assert certifi.__version__ == "2026.10.23", certifi.__version__
+assert msgpack.__version__ == "1.2.3", msgpack.__version__
+assert urllib3.__version__ == "2.8.0", urllib3.__version__
+assert "br" not in urllib3.util.request.ACCEPT_ENCODING, urllib3.util.request.ACCEPT_ENCODING
+assert urllib3.util.request.ACCEPT_ENCODING in {"gzip,deflate", "gzip,deflate,zstd"}
+assert Path(certifi.where()).as_posix().endswith("/pip/_vendor/certifi/cacert.pem"), certifi.where()
+assert certifi.contents() == Path(certifi.where()).read_text(encoding="ascii")
+assert str(pkg_resources.Requirement.parse("openxflow>=1")) == "openxflow>=1"
+PY
+
+"$python_bin" -m pip --version >/dev/null
diff --git a/manifest.json b/manifest.json
new file mode 100644
index 0000000000..7ce34b5de3
--- /dev/null
+++ b/manifest.json
@@ -0,0 +1 @@
+[{"Config":"blobs/sha256/ff65942a9f4bdb543e6d90d6f4834bb92cb3b5e04eb9e1b9e77ce304559de60b","RepoTags":["openxflow-pr57:full-amd64"],"Layers":["blobs/sha256/96e018865a7455542a612b44ed5f5973fa5ce3706b35273d9256f2da3960156c","blobs/sha256/36852ffdf63142ad784f80d84ccde06a29c462ba6ba89ffc1639a9f766159f52","blobs/sha256/5f0ff743861c4f800a0e31b9671e7754235a2843ae0b65acbfa57405e06b6439","blobs/sha256/3cc11d298f30120b8302367aaf8b616ea35fbc3dedc63d63a58a210deec16941","blobs/sha256/a2910a83c190941413d4db5adbcce218b747b26f2c345bfca99420f511a251bd","blobs/sha256/3577ed697fa12d23c5f7e2f35f8fa662d573a8e4eb7613ab4336438c13bdb7d9","blobs/sha256/bc48d9be320784ff58d458799adb527de50d81a1d4cb2894d53df1ed38021bc7","blobs/sha256/e8a75d73af8640e64bce8df6412212c70f394d6c925199f53a87c197653d4b5d","blobs/sha256/bbcc79e23e8cc2451e85dc257f2416f84c314281809cc5e11c4bf79546ea1ec7","blobs/sha256/a828bf71f252486f6765928620429558de92a3eb50b905b3a70611aaf8a2b127","blobs/sha256/df2a818a9ab437b13eeb01b4b20f1af95caeffac0befb87e3e91328fe2f37bce","blobs/sha256/4e9ff8b61f30a6edb8d27d6feb482bb126caec4bc3d39b89e5a8f6817f29b7de","blobs/sha256/4ee2e186ab765c5f8f4fe9f6790b1036435b6b1e3fcb10bf3a882a2d958b663f","blobs/sha256/4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1","blobs/sha256/1e13dd49875c01a6e952f955d1543d0507558da4ae5163811f51c662e7b0e76d"]}]
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index fb1d602f1d..f0aa963861 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -186,11 +186,11 @@ bundles = [
     "lfx-bundles[all-no-torch]>=1.2.0,<2.0",
     "lfx-arxiv>=0.2.0,<1.0.0",
     "lfx-confluent>=0.2.0,<1.0.0",
-    "lfx-duckduckgo>=0.2.0,<1.0.0",
+    "lfx-duckduckgo>=0.2.1,<1.0.0",
     "lfx-empiriolabs>=0.2.0,<1.0.0",
     "lfx-exa>=0.2.0,<1.0.0",
     "lfx-firecrawl>=0.2.0,<1.0.0",
-    "lfx-nextplaid>=0.2.0,<1.0.0",
+    "lfx-nextplaid>=0.2.1,<1.0.0",
     "lfx-paddle>=0.2.0,<1.0.0",
     "lfx-serpingapi>=0.1.0,<1.0.0",
     "lfx-serply>=0.1.0,<1.0.0",
@@ -261,7 +261,7 @@ constraint-dependencies = [
     "multidict>=6.9.1",  # GHSA-54p9-h82j-f925 / CVE-2026-104874
     # Security floors for every workspace dependency path, including optional bundles.
     "oauthlib>=4.0.0,<5.0.0",  # CVE-2026-49264, CVE-2026-49265
-    "pymongo>=4.18.2,<5.0.0",  # CVE-2026-88029, CVE-2026-96747, CVE-2026-96748, CVE-2026-96749
+    "pymongo>=4.18.3,<5.0.0",  # Includes CVE-2026-106435 and prior fixes
     "tornado>=6.5.10,<7.0.0",  # 6.5.8/6.5.9 security fixes plus Jupyter compatibility fix
 ]
 override-dependencies = [
@@ -270,12 +270,19 @@ override-dependencies = [
     # Security: Force upgraded versions to prevent transitive deps from pulling older vulnerable versions
     "orjson>=3.11.6",
     "gunicorn>=25.3.0",
-    "pypdf>=6.19.0,<7.0.0",  # Force a patched pypdf 6.x release
+    "pypdf>=6.21.2,<7.0.0",  # Bound recursive PDF parsing and malformed page traversal.
+    "black>=26.10.1,<27.0.0",  # CVE-2026-114134
+    "datamodel-code-generator>=0.81.0,<0.87.0",  # CVE-2026-107377: Protobuf import path traversal.
+    "setuptools>=83.0.0,<84.0.0",
+    # 4.2.1 fixes ReadWriteLock races but requires Python >=3.11. 3.20.3
+    # predates ReadWriteLock (3.20.4); older Unix/Windows MEDIUM issues remain.
+    "filelock>=3.20.3,<3.20.4; python_version < '3.11'",
+    "filelock>=4.2.1,<5.0.0; python_version >= '3.11'",
     "cryptography>=50.0.0",  # GHSA-g6cj-pr64-35w5 is fixed in 50.0.0
     "nltk>=3.10.3",
     "Markdown>=3.8.0",
     "playwright>=1.59.0",  # Latest available on PyPI; ensures updated Chromium with CVE fixes
-    "authlib>=1.8.0,<2.0.0",  # Scanner reported authlib 1.7.2; use the latest compatible release.
+    "authlib>=1.9.1,<2.0.0",  # CVE-2026-96760 / CVE-2026-114615 / CVE-2026-114616
     "PyJWT>=2.15.1,<3.0.0",  # CVE-2026-102266, CVE-2026-102267, CVE-2026-102271, CVE-2026-102272, CVE-2026-102274
     "virtualenv>=21.14.2,<22.0.0",  # Scanner reported virtualenv 21.7.0; use the latest compatible release.
     "Werkzeug>=3.1.9,<4.0.0",  # Scanner reported Werkzeug 3.1.8; use the latest compatible release.
@@ -286,12 +293,20 @@ override-dependencies = [
     # cryptography below 49; 0.5.1 is the newest release without that cap.
     "langchain-litellm==0.5.1",
     # Transitive dependency CVE fixes
+    # Active production image findings (Trivy run 39888891512).
+    "aiohttp>=3.14.4,<4.0.0",  # CVE-2026-111064 / CVE-2026-111066
+    "certifi>=2026.10.19",  # GHSA-6q48-6c7w-9gw2
+    "msgpack>=1.2.3,<2.0.0",  # CVE-2026-114613 and GHSA-6v7p-g79w-8964
+    "dnspython>=2.9.0,<3.0.0; python_version >= '3.11'",  # CVE-2026-114605; fixed version needs Python 3.11+
+    "dnspython>=2.8.0,<2.9.0; python_version < '3.11'",  # Python 3.10 compatibility; must not ship in runtime 3.14
     "lxml>=6.1.0,<7.0.0",  # CVE-2026-41066
     "mako>=1.3.12,<2.0.0",  # CVE-2026-44307
     "urllib3>=2.8.0,<3.0.0",  # CVE-2026-97688
     "python-liquid>=2.2.0,<3.0.0",  # CVE-2026-45017
     "GitPython>=3.1.58",  # GHSA-wvpp-8hx9-p66j, GHSA-jm78-9fvv-mhgr, GHSA-hmq2-w58f-27jc, GHSA-4gmw-gg2m-w46p, GHSA-9rj7-rf2p-w77r, GHSA-hh9p-6wh2-4mfc
     "h2>=4.4.1",  # CVE-2026-71554
+    "brotli>=1.2.1,<2.0.0; platform_python_implementation == 'CPython'",  # CVE-2026-111497
+    "brotlicffi>=1.2.1.0,<2.0.0; platform_python_implementation != 'CPython'",  # CVE-2026-111497
 ]
 
 [tool.codespell]
diff --git a/scripts/ci/openxflow_verify_image.py b/scripts/ci/openxflow_verify_image.py
index 131f2bcaec..14da8480c0 100644
--- a/scripts/ci/openxflow_verify_image.py
+++ b/scripts/ci/openxflow_verify_image.py
@@ -16,6 +16,7 @@
 import hashlib
 import json
 import re
+import secrets
 import subprocess
 import tarfile
 import time
@@ -26,6 +27,11 @@
 SEMVER = re.compile(r"(?:0|[1-9][0-9]*)\.(?:0|[1-9][0-9]*)\.(?:0|[1-9][0-9]*)\Z")
 SHA256 = re.compile(r"sha256:[0-9a-f]{64}\Z")
 PLATFORMS = ("amd64", "arm64")
+SMOKE_MIN_PASSWORD_LENGTH = 32
+# arm64 is emulated with QEMU on the AMD64 GitHub runner. Its Python startup
+# and first database migration can take substantially longer than native AMD64.
+SMOKE_STARTUP_TIMEOUT_S = {"amd64": 120, "arm64": 600}
+SMOKE_POLL_INTERVAL_S = 2
 PREDICATE_PROVENANCE = {
     "https://slsa.dev/provenance/v0.2",
     "https://slsa.dev/provenance/v1",
@@ -173,6 +179,40 @@ def validate_oci_index(index: dict[str, Any], files: dict[str, bytes]) -> dict[s
     return result
 
 
+def resolve_inspected_oci_index(
+    archive_index: dict[str, Any], files: dict[str, bytes], inspected: dict[str, Any], raw: bytes
+) -> dict[str, Any]:
+    """Match Skopeo's selected index to the exact image stored in the OCI layout.
+
+    Buildx OCI exports may wrap the multi-platform image index in a single
+    descriptor in index.json. Skopeo resolves that descriptor and prints the
+    referenced index rather than the outer layout index. Prove the selected
+    index is the SHA-256-addressed blob in the archive before accepting it.
+    """
+    _require(isinstance(inspected.get("manifests"), list), "skopeo raw inspection has no image manifests")
+    if inspected == archive_index:
+        return archive_index
+
+    descriptors = archive_index.get("manifests") or []
+    _require(len(descriptors) == 1, "ambiguous OCI layout index; expected a single image-index descriptor")
+    descriptor = descriptors[0]
+    _require(
+        descriptor.get("mediaType")
+        in {
+            "application/vnd.oci.image.index.v1+json",
+            "application/vnd.docker.distribution.manifest.list.v2+json",
+        },
+        "OCI layout descriptor is not a multi-platform image index",
+    )
+    selected_digest = "sha256:" + hashlib.sha256(raw).hexdigest()
+    _require(
+        _descriptor_digest(descriptor) == selected_digest,
+        "skopeo raw index is not referenced by the OCI layout",
+    )
+    _require(_read_blob(files, selected_digest) == raw, "skopeo raw index bytes differ from OCI archive blob")
+    return inspected
+
+
 def skopeo_inspect_archive(archive: Path) -> tuple[dict[str, Any], bytes]:
     raw = _run_required(["skopeo", "inspect", "--raw", f"oci-archive:{archive}"])
     raw_bytes = raw.encode("utf-8")
@@ -209,7 +249,7 @@ def build_trivy_command(archive: Path, scanner: str, report: Path) -> list[str]:
         "HIGH,CRITICAL",
         "--ignore-unfixed=false",
         "--ignorefile",
-        "/dev/null",
+        str(Path(__file__).resolve().parents[2] / ".trivyignore.yaml"),
         "--format",
         "json",
         "--output",
@@ -273,52 +313,119 @@ def _probe(tag: str) -> None:
     )
 
 
-def _smoke(tag: str, arch: str, names: list[str]) -> None:
+def build_smoke_run_command(tag: str, name: str, password: str) -> list[str]:
+    """Run offline with an ephemeral superuser; never weaken image auth defaults."""
+    _require(len(password) >= SMOKE_MIN_PASSWORD_LENGTH, "smoke-test password must be strong")
+    return [
+        "docker",
+        "run",
+        "--detach",
+        "--network",
+        "none",
+        "--user",
+        "1000:0",
+        "--name",
+        name,
+        "--env",
+        "LANGFLOW_DATABASE_URL=sqlite:////app/data/openxflow-verify.db",
+        "--env",
+        "LANGFLOW_CONFIG_DIR=/app/data/config",
+        "--env",
+        "LANGFLOW_AUTO_LOGIN=false",
+        "--env",
+        "LANGFLOW_SUPERUSER=langflow",
+        "--env",
+        f"LANGFLOW_SUPERUSER_PASSWORD={password}",
+        "--env",
+        "LANGFLOW_HOST=0.0.0.0",
+        "--env",
+        "LANGFLOW_PORT=7860",
+        "--env",
+        "LANGFLOW_SKIP_MCP_AUTO_INIT=true",
+        "--env",
+        "LANGFLOW_MODELS_DEV_REFRESH=false",
+        "--env",
+        "LANGFLOW_WORKERS=1",
+        "--env",
+        "LITELLM_OFFLINE_MODE=true",
+        "--env",
+        "LITELLM_LOCAL_MODEL_COST_MAP=true",
+        "--env",
+        "DO_NOT_TRACK=true",
+        tag,
+    ]
+
+
+def smoke_startup_timeout(arch: str) -> int:
+    """Allow QEMU-emulated ARM64 enough time without weakening readiness."""
+    _require(arch in SMOKE_STARTUP_TIMEOUT_S, f"unsupported smoke-test architecture: {arch}")
+    return SMOKE_STARTUP_TIMEOUT_S[arch]
+
+
+def _smoke_diagnostics(name: str) -> str:
+    """Include container exit state and *both* Docker log streams."""
+    state = _run(["docker", "inspect", "--format", "{{.State.Status}} exit={{.State.ExitCode}}", name])
+    state_summary = (state.stdout or state.stderr).strip() or "unavailable"
+    logs = _run(["docker", "logs", "--tail", "100", name])
+    output = "\n".join(part for part in (logs.stdout, logs.stderr) if part).strip()
+    return f"container state: {state_summary}\ncontainer logs:\n{output[-4000:] or '(empty)'}"
+
+
+def probe_smoke_endpoints(name: str) -> tuple[bool, str]:
+    """Verify both endpoints with curl, avoiding slow Python startup under QEMU."""
+    for path in ("/health_check", "/"):
+        command = [
+            "docker",
+            "exec",
+            name,
+            "curl",
+            "--fail",
+            "--silent",
+            "--show-error",
+            "--connect-timeout",
+            "2",
+            "--max-time",
+            "5",
+            f"http://127.0.0.1:7860{path}",
+        ]
+        result = _run(command)
+        if result.returncode or not result.stdout.strip():
+            detail = (result.stderr or result.stdout).strip()[-500:]
+            return False, f"{path}: {detail or 'empty or unsuccessful HTTP response'}"
+    return True, ""
+
+
+def smoke_image(tag: str, arch: str, names: list[str]) -> None:
+    """Require a fully started, authenticated HTTP service before accepting an image."""
     name = f"openxflow-verify-{uuid.uuid4().hex[:16]}-{arch}"
     names.append(name)
+    # Credentials are unique to this isolated, disposable container. They are
+    # never printed, committed, or embedded into the production image.
+    password = secrets.token_urlsafe(32)
     try:
-        result = _run(
-            [
-                "docker",
-                "run",
-                "--detach",
-                "--network",
-                "none",
-                "--user",
-                "1000:0",
-                "--name",
-                name,
-                "--env",
-                "LANGFLOW_DATABASE_URL=sqlite:////app/data/openxflow-verify.db",
-                "--env",
-                "LANGFLOW_CONFIG_DIR=/app/data/config",
-                "--env",
-                "LANGFLOW_AUTO_LOGIN=false",
-                "--env",
-                "LANGFLOW_HOST=0.0.0.0",
-                "--env",
-                "LANGFLOW_PORT=7860",
-                tag,
-            ]
-        )
+        result = _run(build_smoke_run_command(tag, name, password))
         if result.returncode:
             raise VerificationError(f"docker smoke container failed to start ({arch}): {result.stderr[-1000:]}")
-        script = """\
-import urllib.request
-for path in ('/health_check', '/'):
-    response = urllib.request.urlopen('http://127.0.0.1:7860' + path, timeout=3)
-    body = response.read()
-    assert response.status == 200 and body, path
-print('smoke pass')
-"""
-        deadline = time.monotonic() + 90
+        deadline = time.monotonic() + smoke_startup_timeout(arch)
+        last_probe = ""
         while time.monotonic() < deadline:
-            probe = _run(["docker", "exec", name, "python", "-c", script])
-            if probe.returncode == 0:
+            ready, last_probe = probe_smoke_endpoints(name)
+            if ready:
                 return
-            time.sleep(2)
-        logs = _run(["docker", "logs", name]).stdout[-2000:]
-        raise VerificationError(f"docker smoke did not reach /health_check and / ({arch})\n{logs}")
+            running = _run(["docker", "inspect", "--format", "{{.State.Running}}", name])
+            if running.returncode or running.stdout.strip().lower() != "true":
+                diagnostics = _smoke_diagnostics(name)
+                raise VerificationError(
+                    f"docker smoke container exited before readiness ({arch})\n"
+                    f"{diagnostics}\nlast readiness probe: {last_probe}"
+                )
+            time.sleep(SMOKE_POLL_INTERVAL_S)
+        diagnostics = _smoke_diagnostics(name)
+        raise VerificationError(
+            f"docker smoke did not reach /health_check and / ({arch}) "
+            f"within {smoke_startup_timeout(arch)}s\n"
+            f"{diagnostics}\nlast readiness probe: {last_probe}"
+        )
     finally:
         _run(["docker", "rm", "--force", name])
 
@@ -342,10 +449,8 @@ def verify(args: argparse.Namespace) -> dict[str, Any]:
         inspected, raw = skopeo_inspect_archive(archive)
         identity["digest"] = "sha256:" + hashlib.sha256(raw.strip()).hexdigest()
         index, files = read_oci_archive(archive)
-        _require(
-            inspected.get("manifests") == index.get("manifests"), "skopeo raw index differs from OCI archive index"
-        )
-        structure = validate_oci_index(index, files)
+        selected_index = resolve_inspected_oci_index(index, files, inspected, raw)
+        structure = validate_oci_index(selected_index, files)
         report["attestations"] = {}
         attestation_dir = output / "attestations"
         attestation_dir.mkdir(exist_ok=True)
@@ -367,7 +472,7 @@ def verify(args: argparse.Namespace) -> dict[str, Any]:
             image = _docker_inspect(tag)
             _inspect_image(image, plan)
             _probe(tag)
-            _smoke(tag, arch, cleanup_names)
+            smoke_image(tag, arch, cleanup_names)
             arch_findings = {"platform": arch, "trivy": []}
             for scanner in ("vuln", "secret"):
                 trivy_report = output / f"trivy-{arch}-{scanner}.json"
diff --git a/scripts/ci/test_ci_workflow.py b/scripts/ci/test_ci_workflow.py
new file mode 100644
index 0000000000..f54460b250
--- /dev/null
+++ b/scripts/ci/test_ci_workflow.py
@@ -0,0 +1,27 @@
+"""Regression checks for CI checkout refs that differ between push and PR events."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def _load(path: str) -> dict:
+    class Loader(yaml.SafeLoader):
+        pass
+
+    # PyYAML 1.1 treats the YAML 1.2 ``on`` key as a boolean.
+    Loader.add_constructor("tag:yaml.org,2002:bool", lambda loader, node: loader.construct_scalar(node))
+    return yaml.load((ROOT / path).read_text(encoding="utf-8"), Loader=Loader)  # noqa: S506
+
+
+def test_object_storage_uses_a_stable_pr_checkout_ref() -> None:
+    workflow = _load(".github/workflows/ci.yml")
+    checkout = next(
+        step for step in workflow["jobs"]["test-object-storage"]["steps"] if step.get("name") == "Checkout code"
+    )
+
+    assert checkout["with"]["ref"] == "${{ inputs.ref || github.event.pull_request.head.sha || github.sha }}"
diff --git a/scripts/ci/test_nightly_status.py b/scripts/ci/test_nightly_status.py
new file mode 100644
index 0000000000..791be0e447
--- /dev/null
+++ b/scripts/ci/test_nightly_status.py
@@ -0,0 +1,38 @@
+"""Regression checks for the CI nightly status gate."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def _load_ci_workflow() -> dict:
+    class Loader(yaml.SafeLoader):
+        pass
+
+    # PyYAML 1.1 treats the YAML 1.2 ``on`` key as a boolean.
+    Loader.add_constructor("tag:yaml.org,2002:bool", lambda loader, node: loader.construct_scalar(node))
+    return yaml.load((ROOT / ".github/workflows/ci.yml").read_text(encoding="utf-8"), Loader=Loader)  # noqa: S506
+
+
+def test_nightly_gate_skips_noncanonical_repositories() -> None:
+    workflow = _load_ci_workflow()
+    check_step = next(
+        step for step in workflow["jobs"]["check-nightly-status"]["steps"] if step.get("id") == "check-pypi"
+    )
+    run_script = check_step["run"]
+    skip_condition = '"${{ github.repository }}" != "langflow-ai/langflow"'
+
+    assert skip_condition in run_script
+    assert run_script.index(skip_condition) < run_script.index("https://pypi.org/pypi/langflow/json")
+    assert "success=true" in run_script
+    assert 'echo "status=not-applicable" >> $GITHUB_OUTPUT' in run_script
+    assert "https://pypi.org/pypi/langflow/json" in run_script
+    outputs = workflow["jobs"]["check-nightly-status"]["outputs"]
+    assert outputs["should-proceed"] == "${{ steps.check-pypi.outputs.success }}"
+    assert outputs["status"] == "${{ steps.check-pypi.outputs.status }}"
+    ci_success = workflow["jobs"]["ci_success"]
+    assert "needs.check-nightly-status.outputs.should-proceed" in ci_success["env"]["EXIT_CODE"]
diff --git a/scripts/ci/test_openxflow_docker_contract.py b/scripts/ci/test_openxflow_docker_contract.py
index 98e61f8d47..67fa25b341 100644
--- a/scripts/ci/test_openxflow_docker_contract.py
+++ b/scripts/ci/test_openxflow_docker_contract.py
@@ -33,6 +33,16 @@ def test_runtime_image_has_openxflow_provenance_and_upstream_attribution() -> No
     assert "FROM runtime AS full" in dockerfile
 
 
+def test_all_image_targets_inherit_exposed_http_port_from_runtime() -> None:
+    """Docker inspect must report 7860/tcp for base, full and bundle images."""
+    runtime = _stage("runtime")
+    assert "LANGFLOW_HOST=0.0.0.0" in runtime
+    assert "LANGFLOW_PORT=7860" in runtime
+    assert re.search(r"(?m)^EXPOSE 7860/tcp$", runtime)
+    for target in ("base", "full", "full-bundles"):
+        assert f"FROM runtime AS {target}" in DOCKERFILE.read_text(encoding="utf-8")
+
+
 def test_production_workflow_never_emits_latest() -> None:
     workflow = WORKFLOW.read_text(encoding="utf-8")
     assert ":latest" not in workflow.lower()
@@ -80,6 +90,91 @@ def test_base_image_is_immutable_and_node_download_is_checked() -> None:
         assert re.search(rf"{arch}\).*?NODE_SHA256=[a-f0-9]{{64}}", builder)
 
 
+def test_runtime_node_images_include_the_brotli_security_fix() -> None:
+    for name in (
+        "build_and_push.Dockerfile",
+        "build_and_push_backend.Dockerfile",
+        "build_and_push_ep.Dockerfile",
+    ):
+        source = (REPO_ROOT / "docker" / name).read_text(encoding="utf-8")
+        match = re.search(r"(?m)^ARG NODE_VERSION=(\d+)\.(\d+)\.(\d+)$", source)
+        assert match is not None, name
+        assert tuple(map(int, match.groups())) >= (24, 22, 1), name
+
+
+def test_runtime_node_checksum_printf_uses_a_real_newline() -> None:
+    for name in (
+        "build_and_push.Dockerfile",
+        "build_and_push_backend.Dockerfile",
+        "build_and_push_ep.Dockerfile",
+    ):
+        source = (REPO_ROOT / "docker" / name).read_text(encoding="utf-8")
+        assert "printf '%s  /tmp/node.tar.xz\\n' \"$NODE_SHA256\"" in source, name
+        assert "printf '%s  /tmp/node.tar.xz\\\\n' \"$NODE_SHA256\"" not in source, name
+
+
+def test_final_virtualenvs_apply_the_pip_vendor_patch_after_copy() -> None:
+    targets = {
+        "build_and_push.Dockerfile": ("base", "full-bundles", "full"),
+        "build_and_push_backend.Dockerfile": ("runtime",),
+        "build_and_push_ep.Dockerfile": ("runtime",),
+    }
+    for name, stage_names in targets.items():
+        source = (REPO_ROOT / "docker" / name).read_text(encoding="utf-8")
+        for stage_name in stage_names:
+            stage = _stage(stage_name) if name == "build_and_push.Dockerfile" else source.split(" AS runtime\n", 1)[1]
+            copy_match = re.search(r"(?m)^COPY --from=.* /app/\.venv /app/\.venv$", stage)
+            assert copy_match is not None, (name, stage_name)
+            copy = copy_match.start()
+            patch = stage.index("COPY ./docker/patch_hardened_pip_vendor.sh", copy_match.end())
+            run = stage.index("sh /tmp/patch_hardened_pip_vendor.sh", patch)
+            assert copy < patch < run
+            assert "find /app/.venv -type d -path '*/pip/_vendor' -exec chown -R 1000:0 {} +" in stage[run:]
+
+
+def test_brotli_lock_constraints_include_the_security_fix() -> None:
+    try:
+        import tomllib
+    except ModuleNotFoundError:  # Python 3.10
+        import tomli as tomllib
+
+    project = tomllib.loads((REPO_ROOT / "pyproject.toml").read_text(encoding="utf-8"))
+    overrides = project["tool"]["uv"]["override-dependencies"]
+    assert "brotli>=1.2.1,<2.0.0; platform_python_implementation == 'CPython'" in overrides
+    assert "brotlicffi>=1.2.1.0,<2.0.0; platform_python_implementation != 'CPython'" in overrides
+
+    base = tomllib.loads((REPO_ROOT / "src/backend/base/pyproject.toml").read_text(encoding="utf-8"))
+    assert "ddgs>=9.0.0" in base["project"]["optional-dependencies"]["duckduckgo"]
+    assert (
+        "brotli>=1.2.1,<2.0.0; platform_python_implementation == 'CPython'"
+        in base["project"]["optional-dependencies"]["duckduckgo"]
+    )
+    assert (
+        "brotlicffi>=1.2.1.0,<2.0.0; platform_python_implementation != 'CPython'"
+        in base["project"]["optional-dependencies"]["duckduckgo"]
+    )
+
+    bundle = tomllib.loads((REPO_ROOT / "src/bundles/duckduckgo/pyproject.toml").read_text(encoding="utf-8"))
+    assert "brotli>=1.2.1,<2.0.0; platform_python_implementation == 'CPython'" in bundle["project"]["dependencies"]
+    assert (
+        "brotlicffi>=1.2.1.0,<2.0.0; platform_python_implementation != 'CPython'" in bundle["project"]["dependencies"]
+    )
+
+    lock = tomllib.loads((REPO_ROOT / "uv.lock").read_text(encoding="utf-8"))
+    httpx = next(package for package in lock["package"] if package["name"] == "httpx")
+    assert {
+        dependency["name"]: dependency.get("marker") for dependency in httpx["optional-dependencies"]["brotli"]
+    } == {
+        "brotli": "platform_python_implementation == 'CPython'",
+        "brotlicffi": "platform_python_implementation != 'CPython'",
+    }
+    assert {
+        package["name"]: package["version"]
+        for package in lock["package"]
+        if package["name"] in {"brotli", "brotlicffi"}
+    } == {"brotli": "1.2.1", "brotlicffi": "1.2.1.0"}
+
+
 def test_context_excludes_local_state_and_secret_files() -> None:
     patterns = set((REPO_ROOT / ".dockerignore").read_text(encoding="utf-8").splitlines())
     assert {
diff --git a/scripts/ci/test_openxflow_image_verification.py b/scripts/ci/test_openxflow_image_verification.py
index ff8dcb017c..dfa7ca1db5 100644
--- a/scripts/ci/test_openxflow_image_verification.py
+++ b/scripts/ci/test_openxflow_image_verification.py
@@ -4,6 +4,7 @@
 
 import hashlib
 import json
+import subprocess
 from pathlib import Path
 
 import pytest
@@ -11,7 +12,11 @@
 from scripts.ci.openxflow_verify_image import (
     VerificationError,
     build_copy_command,
+    build_smoke_run_command,
     build_trivy_command,
+    probe_smoke_endpoints,
+    resolve_inspected_oci_index,
+    smoke_startup_timeout,
     summarize_trivy,
     validate_oci_index,
     validate_plan,
@@ -99,7 +104,7 @@ def test_commands_pin_overrides_and_strict_trivy_policy(tmp_path: Path) -> None:
     assert "amd64" in copy
     trivy = build_trivy_command(tmp_path / "amd64.tar", "vuln", tmp_path / "vuln.json")
     assert "--ignore-unfixed=false" in trivy
-    assert trivy[trivy.index("--ignorefile") + 1] == "/dev/null"
+    assert Path(trivy[trivy.index("--ignorefile") + 1]).name == ".trivyignore.yaml"
 
 
 def test_trivy_summary_counts_findings(tmp_path: Path) -> None:
@@ -109,3 +114,152 @@ def test_trivy_summary_counts_findings(tmp_path: Path) -> None:
     )
     summary = summarize_trivy(report, "vuln")
     assert summary["finding_count"] == 1
+
+
+def test_skopeo_selected_index_matches_single_sha256_addressed_oci_layout_blob() -> None:
+    """An OCI layout wrapper is legitimate only when its selected digest matches."""
+    selected, files = _archive_files()
+    raw = json.dumps(selected, separators=(",", ":")).encode("utf-8")
+    digest, _ = _blob(raw)
+    files[f"blobs/sha256/{digest[7:]}"] = raw
+    layout = {
+        "schemaVersion": 2,
+        "manifests": [
+            {
+                "mediaType": "application/vnd.oci.image.index.v1+json",
+                "digest": digest,
+                "size": len(raw),
+                "annotations": {"org.opencontainers.image.ref.name": "openxflow"},
+            }
+        ],
+    }
+    verified = resolve_inspected_oci_index(layout, files, selected, raw)
+    assert set(validate_oci_index(verified, files)["platforms"]) == {"amd64", "arm64"}
+
+
+def test_skopeo_index_identity_fails_closed_on_foreign_or_tampered_blobs() -> None:
+    selected, files = _archive_files()
+    raw = json.dumps(selected).encode("utf-8")
+    digest, _ = _blob(raw)
+    files[f"blobs/sha256/{digest[7:]}"] = raw
+    layout = {
+        "schemaVersion": 2,
+        "manifests": [{"mediaType": "application/vnd.oci.image.index.v1+json", "digest": digest}],
+    }
+    assert resolve_inspected_oci_index(selected, files, selected, raw) == selected
+    with pytest.raises(VerificationError, match="not referenced"):
+        resolve_inspected_oci_index(layout, files, selected, raw + b" ")
+    files[f"blobs/sha256/{digest[7:]}"] = b"tampered"
+    with pytest.raises(VerificationError, match="digest mismatch"):
+        resolve_inspected_oci_index(layout, files, selected, raw)
+    layout["manifests"].append(dict(layout["manifests"][0]))
+    with pytest.raises(VerificationError, match="ambiguous"):
+        resolve_inspected_oci_index(layout, files, selected, raw)
+
+
+def test_authenticated_offline_smoke_requires_ephemeral_superuser_credentials() -> None:
+    """CI must supply credentials while preserving production's no-auto-login default."""
+    password = "x" * 48  # Synthetic input, not a real credential.
+    command = build_smoke_run_command("image:test", "smoke-one", password)
+    assert command[:4] == ["docker", "run", "--detach", "--network"]
+    assert command[4] == "none"
+    assert command[-1] == "image:test"
+    envs = [command[pos + 1] for pos, item in enumerate(command[:-1]) if item == "--env"]
+    assert "LANGFLOW_AUTO_LOGIN=false" in envs
+    assert "LANGFLOW_SUPERUSER=langflow" in envs
+    assert f"LANGFLOW_SUPERUSER_PASSWORD={password}" in envs
+    assert "LANGFLOW_DATABASE_URL=sqlite:////app/data/openxflow-verify.db" in envs
+    assert "LANGFLOW_CONFIG_DIR=/app/data/config" in envs
+    assert "--user" in command
+    assert command[command.index("--user") + 1] == "1000:0"
+    with pytest.raises(VerificationError, match="must be strong"):
+        build_smoke_run_command("image:test", "smoke-one", "short")
+
+
+def test_emulated_arm64_smoke_has_bounded_longer_startup_window() -> None:
+    assert smoke_startup_timeout("arm64") > smoke_startup_timeout("amd64") > 0
+    with pytest.raises(VerificationError, match="unsupported"):
+        smoke_startup_timeout("other")
+
+
+def test_failed_smoke_reports_stderr_and_exits_early(monkeypatch: pytest.MonkeyPatch) -> None:
+    from scripts.ci import openxflow_verify_image as verifier
+
+    calls: list[list[str]] = []
+
+    def fake_run(args: list[str], **_kwargs: object) -> subprocess.CompletedProcess[str]:
+        calls.append(args)
+        if args[:2] == ["docker", "exec"]:
+            return subprocess.CompletedProcess(args, 1, "", "connection refused")
+        if args[:3] == ["docker", "inspect", "--format"]:
+            return subprocess.CompletedProcess(args, 0, "false\n", "")
+        if args[:2] == ["docker", "logs"]:
+            return subprocess.CompletedProcess(args, 0, "", "Startup failed: missing database")
+        return subprocess.CompletedProcess(args, 0, "container-id", "")
+
+    monkeypatch.setattr(verifier, "_run", fake_run)
+    names: list[str] = []
+    with pytest.raises(VerificationError, match="Startup failed: missing database"):
+        verifier.smoke_image("image:test", "arm64", names)
+    assert len(names) == 1
+    assert any(command[:2] == ["docker", "rm"] for command in calls)
+    assert not any(command[:2] == ["docker", "exec"] for command in calls[2:])
+
+
+def test_smoke_environment_skips_network_refresh_but_keeps_authentication() -> None:
+    command = build_smoke_run_command("image:test", "smoke-two", "x" * 48)
+    variables = [command[i + 1] for i, part in enumerate(command[:-1]) if part == "--env"]
+    assert "LANGFLOW_AUTO_LOGIN=false" in variables
+    assert "LANGFLOW_SKIP_MCP_AUTO_INIT=true" in variables
+    assert "LANGFLOW_MODELS_DEV_REFRESH=false" in variables
+    assert "LANGFLOW_WORKERS=1" in variables
+    assert "LITELLM_LOCAL_MODEL_COST_MAP=true" in variables
+    assert "LITELLM_OFFLINE_MODE=true" in variables
+    assert "DO_NOT_TRACK=true" in variables
+
+
+def test_curl_smoke_probe_requires_both_real_http_endpoints(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    from scripts.ci import openxflow_verify_image as verifier
+
+    requests: list[list[str]] = []
+
+    def successful_http(args: list[str], **_kwargs: object) -> subprocess.CompletedProcess[str]:
+        requests.append(args)
+        return subprocess.CompletedProcess(args, 0, "healthy", "")
+
+    monkeypatch.setattr(verifier, "_run", successful_http)
+    assert probe_smoke_endpoints("test-container") == (True, "")
+    assert len(requests) == 2
+    assert requests[0][-1] == "http://127.0.0.1:7860/health_check"
+    assert requests[1][-1] == "http://127.0.0.1:7860/"
+    for command in requests:
+        assert command[:4] == ["docker", "exec", "test-container", "curl"]
+        assert "--fail" in command
+        assert "--max-time" in command
+
+    requests.clear()
+
+    def failed_http(args: list[str], **_kwargs: object) -> subprocess.CompletedProcess[str]:
+        requests.append(args)
+        return subprocess.CompletedProcess(args, 22, "", "HTTP 503")
+
+    monkeypatch.setattr(verifier, "_run", failed_http)
+    ready, reason = probe_smoke_endpoints("test-container")
+    assert not ready
+    assert "503" in reason
+    assert len(requests) == 1
+
+
+def test_curl_smoke_probe_rejects_empty_http_response(monkeypatch: pytest.MonkeyPatch) -> None:
+    from scripts.ci import openxflow_verify_image as verifier
+
+    monkeypatch.setattr(
+        verifier,
+        "_run",
+        lambda args: subprocess.CompletedProcess(args, 0, "", ""),
+    )
+    ready, reason = probe_smoke_endpoints("test-container")
+    assert not ready
+    assert "/health_check" in reason
diff --git a/scripts/ci/test_openxflow_release_workflow.py b/scripts/ci/test_openxflow_release_workflow.py
index 21223a9121..923b3c9699 100644
--- a/scripts/ci/test_openxflow_release_workflow.py
+++ b/scripts/ci/test_openxflow_release_workflow.py
@@ -19,10 +19,13 @@ class Loader(yaml.SafeLoader):
     return yaml.load(WORKFLOW.read_text(encoding="utf-8"), Loader=Loader)
 
 
-def test_only_closed_merged_main_pr_can_start_workflow():
+def test_workflow_builds_pr_artifacts_and_publishes_only_after_merge():
     workflow = load()
     assert set(workflow["on"]) == {"pull_request"}
-    assert workflow["on"]["pull_request"] == {"types": ["closed"], "branches": ["main"]}
+    assert workflow["on"]["pull_request"] == {
+        "types": ["opened", "synchronize", "reopened", "closed"],
+        "branches": ["main"],
+    }
     admission = workflow["jobs"]["validate-merge"]["if"]
     for expression in [
         "github.repository == 'lien0219/openxflow'",
@@ -33,10 +36,68 @@ def test_only_closed_merged_main_pr_can_start_workflow():
         "github.event.pull_request.merge_commit_sha != ''",
     ]:
         assert expression in admission
+    preview = workflow["jobs"]["preview-package"]
+    assert "github.event.action != 'closed'" in preview["if"]
+    checkout = next(step for step in preview["steps"] if step.get("name") == "Check out PR head")
+    assert checkout["with"]["ref"] == "${{ github.event.pull_request.head.sha }}"
+    build = next(step for step in preview["steps"] if step.get("name") == "Build the PR OCI package")
+    assert build["with"]["push"] == "false"
+    assert build["with"]["outputs"] == "type=oci,dest=/tmp/openxflow-pr.oci.tar"
+    assert any(step.get("uses", "").startswith("actions/upload-artifact@") for step in preview["steps"])
     for forbidden in ["workflow_dispatch", "workflow_call", "workflow_run", "schedule", "pull_request_target"]:
         assert forbidden not in workflow["on"]
 
 
+def test_scanners_use_pinned_resolvable_trivy_action():
+    workflow = load()
+    expected = "aquasecurity/trivy-action@ed142fd0673e97e23eac54620cfb913e5ce36c25"
+    for job_name, prefix in (
+        ("preview-package", "/tmp/openxflow-pr"),  # noqa: S108 - workflow fixture path
+        ("build-and-verify", "/tmp/openxflow"),  # noqa: S108 - workflow fixture path
+    ):
+        steps = workflow["jobs"][job_name]["steps"]
+        prepare = next(step for step in steps if step.get("name", "").startswith("Prepare per-architecture Trivy"))
+        assert "skopeo copy --override-os linux --override-arch" in prepare["run"]
+        assert "docker-archive:" in prepare["run"]
+        for arch in ("amd64", "arm64"):
+            scans = [
+                step
+                for step in steps
+                if step.get("name", "").endswith(f"{arch} image")
+                and step.get("uses", "").startswith("aquasecurity/trivy-action@")
+            ]
+            assert len(scans) == 1
+            assert scans[0]["uses"] == expected
+            assert scans[0]["with"]["input"] == f"{prefix}-{arch}.docker.tar"
+            assert scans[0]["with"]["exit-code"] == "1"
+            assert scans[0]["with"]["severity"] == "CRITICAL,HIGH"
+            assert scans[0]["with"]["scanners"] == "vuln,secret"
+
+
+def test_trivy_reports_survive_failed_security_scans():
+    workflow = load()
+    for job, profile in (
+        ("preview-package", "PR"),
+        ("build-and-verify", "production"),
+    ):
+        steps = workflow["jobs"][job]["steps"]
+        output_prefix = profile.lower()
+        for arch in ("amd64", "arm64"):
+            scan = next(step for step in steps if step.get("name") == f"Scan the unpushed {profile} {arch} image")
+            assert scan["with"]["format"] == "json"
+            assert scan["with"]["output"] == f"/tmp/openxflow-trivy/{output_prefix}-{arch}.json"  # noqa: S108 - workflow artifact path contract
+            assert scan["with"]["exit-code"] == "1"
+            assert scan["with"]["ignore-unfixed"] == "false"
+            assert scan["with"]["severity"] == "CRITICAL,HIGH"
+            assert scan["with"]["scanners"] == "vuln,secret"
+            assert scan.get("continue-on-error") in (None, False, "false")
+        retention = next(
+            step for step in steps if step.get("name", "").startswith("Retain ") and "Trivy" in step.get("name", "")
+        )
+        assert retention["if"] == "always()"
+        assert retention["with"]["path"] == "/tmp/openxflow-trivy/"  # noqa: S108 - workflow artifact path contract
+
+
 def test_build_has_no_registry_write_before_scan():
     source = WORKFLOW.read_text(encoding="utf-8")
     build_start = source.index("  build-and-verify:")
@@ -44,7 +105,12 @@ def test_build_has_no_registry_write_before_scan():
     build = source[build_start:publish_start]
     assert "push: false" in build
     assert "outputs: type=oci,dest=/tmp/openxflow.oci.tar" in build
-    assert build.index("Scan the unpushed OCI archive") > build.index("Build a multi-architecture OCI archive")
+    build_at = build.index("Build a multi-architecture OCI archive")
+    prepare_at = build.index("Prepare per-architecture Trivy scan inputs (production)")
+    amd64_at = build.index("Scan the unpushed production amd64 image")
+    arm64_at = build.index("Scan the unpushed production arm64 image")
+    verify_at = build.index("Verify OCI attestations, both architectures, and runtime probes")
+    assert build_at < prepare_at < amd64_at < arm64_at < verify_at
     assert 'skopeo copy --all --preserve-digests "oci-archive:' not in build
 
 
@@ -70,8 +136,42 @@ def test_retry_runtime_probes_use_the_verified_reuse_coordinate():
 
 
 def test_workflow_has_no_untrusted_code_trigger_or_manual_sha_input():
+    workflow = load()
     source = WORKFLOW.read_text(encoding="utf-8")
     assert "pull_request_target" not in source
     assert "workflow_dispatch" not in source
     assert "inputs:" not in source
-    assert "github.event.pull_request.head.sha" not in source
+    assert "github.event.pull_request.head.sha" not in workflow["jobs"]["validate-merge"]["if"]
+
+
+def test_sdk_secret_exception_is_narrow_and_expires():
+    """Only the confirmed SDK false positive may bypass the secret gate."""
+    from datetime import date, datetime, timezone
+
+    exceptions = yaml.safe_load((ROOT / ".trivyignore.yaml").read_text(encoding="utf-8"))
+    assert set(exceptions) == {"secrets"}
+    assert len(exceptions["secrets"]) == 1
+    item = exceptions["secrets"][0]
+    assert set(item) == {"id", "paths", "expired_at", "statement"}
+    assert item["id"] == "gcp-service-account"
+    assert item["paths"] == [
+        "**/site-packages/ibm_watsonx_ai/connections.py",
+        "**/site-packages/ibm_watsonx_ai/__pycache__/connections.cpython-314.pyc",
+    ]
+    # The bytecode false-positive was observed in the actual Trivy JSON evidence
+    # for the amd64 image, not in the source file. Keep its scope exact.
+    from fnmatch import fnmatch
+
+    finding = "/app/.venv/lib/python3.14/site-packages/ibm_watsonx_ai/__pycache__/connections.cpython-314.pyc"
+    assert any(fnmatch(finding, pattern) for pattern in item["paths"])
+    other_module = "/app/.venv/lib/python3.14/site-packages/other/__pycache__/connection.cpython-314.pyc"
+    assert not any(fnmatch(other_module, pattern) for pattern in item["paths"])
+    assert datetime.now(timezone.utc).date() <= item["expired_at"] <= date(2026, 12, 31)
+    assert item["statement"].strip()
+    for job_name in ("preview-package", "build-and-verify"):
+        scans = [s for s in load()["jobs"][job_name]["steps"] if s.get("name", "").startswith("Scan the unpushed")]
+        assert len(scans) == 2
+        for scan in scans:
+            assert scan["with"]["trivyignores"] == ".trivyignore.yaml"
+            assert scan["with"]["scanners"] == "vuln,secret"
+            assert scan["with"]["exit-code"] == "1"
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json
index cc1e9967e9..4eadb21a36 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json	
@@ -524,7 +524,7 @@
             "description": "# 📖 README\nThis template demonstrates a standard chat flow with additional instructions provided by a prompt. Prompts provide instructions and inputs for a Large Language Model (LLM) beyond the standard user-provided chat input. In this example, the prompt describes the LLM's role and persona.\n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Open the **Playground** to start the chat and run the flow.\n\n## Next steps\nChange the prompt template, model, or model settings, such as **Temperature**, and then see how the responses change with these different inputs.\n💡 Some component settings are hidden by default; to view all settings click **Controls** in each component's header menu.\n💡 You can use curly braces to create variables in your template, such as `{variable}`. These can be populated from other components, with OpenXFlow global variables, or at runtime.",
             "display_name": "",
             "documentation": "",
-            "i18n_key": "template_notes.basic_prompting.bd8ff52b",
+            "i18n_key": "template_notes.basic_prompting.ee8855e9",
             "template": {
               "backgroundColor": "neutral"
             }
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json
index d4cc8b1d08..6d12bbcc47 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json	
@@ -152,6 +152,7 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -160,7 +161,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -169,8 +170,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -428,7 +428,8 @@
             "legacy": false,
             "lf_version": "1.10.1rc3",
             "metadata": {
-              "code_hash": "a6429c7f9670",
+              "code_hash": "8052088025a1",
+              "component_class_name": "URLComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -437,21 +438,20 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
-                  },
-                  {
-                    "name": "markitdown",
-                    "version": "0.1.6"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "lfx",
                     "version": null
+                  },
+                  {
+                    "name": "markitdown",
+                    "version": "0.1.6"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.data_source.url.URLComponent",
-              "component_class_name": "URLComponent"
+              "module": "lfx.components.data_source.url.URLComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -535,7 +535,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom markitdown import MarkItDown\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies OpenXFlow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
+                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        # markitdown imports magika, which loads the nearest .env at import time. Keep that\n        # optional converter out of component discovery and load it only when Markdown output\n        # is actually requested.\n        from markitdown import MarkItDown\n\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
               },
               "continue_on_failure": {
                 "_input_type": "BoolInput",
@@ -899,11 +899,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -915,13 +916,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1071,7 +1071,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2283,4 +2283,4 @@
     "chatbots",
     "content-generation"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Content Aggregator.json b/src/backend/base/langflow/initial_setup/starter_projects/Content Aggregator.json
index 7d572fa021..8a140fbb0f 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Content Aggregator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Content Aggregator.json	
@@ -499,6 +499,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -515,7 +516,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -528,8 +529,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1520,11 +1520,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1536,13 +1537,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1692,7 +1692,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2274,6 +2274,7 @@
             "lf_version": "1.4.3",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -2282,7 +2283,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -2291,8 +2292,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -2569,7 +2569,8 @@
             "last_updated": "2025-09-30T16:16:26.172Z",
             "legacy": false,
             "metadata": {
-              "code_hash": "6fb7a6da11ce",
+              "code_hash": "9706742cef6c",
+              "component_class_name": "SaveToFileComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -2582,7 +2583,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -2594,7 +2595,7 @@
                   },
                   {
                     "name": "boto3",
-                    "version": "1.40.61"
+                    "version": "1.43.46"
                   },
                   {
                     "name": "googleapiclient",
@@ -2603,8 +2604,7 @@
                 ],
                 "total_dependencies": 7
               },
-              "module": "lfx.components.files_and_knowledge.save_file.SaveToFileComponent",
-              "component_class_name": "SaveToFileComponent"
+              "module": "lfx.components.files_and_knowledge.save_file.SaveToFileComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2772,7 +2772,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import contextlib\nimport json\nfrom collections.abc import AsyncIterator, Iterator\nfrom pathlib import Path, PurePath, PureWindowsPath\nfrom typing import Any\n\nimport orjson\nimport pandas as pd\nfrom fastapi import UploadFile\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.custom import Component\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DataFrameInput\nfrom lfx.io import BoolInput, DropdownInput, SecretStrInput, StrInput\nfrom lfx.schema import Data, DataFrame, Message\nfrom lfx.services.deps import get_settings_service, get_storage_service, session_scope\nfrom lfx.template.field.base import Output\nfrom lfx.utils.end_user_storage import end_user_folder_segment, record_end_user_folder\nfrom lfx.utils.file_path_security import component_file_access_scopes, enforce_local_file_access\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\ndef _get_default_storage_location() -> list[dict[str, str]]:\n    \"\"\"Return the default storage selection for the component template.\"\"\"\n    return [_get_storage_location_options()[0]]\n\n\ndef _is_default_storage(storage_name: str) -> bool:\n    \"\"\"Check whether a storage type is the default selection.\"\"\"\n    return _get_default_storage_location()[0][\"name\"] == storage_name\n\n\nclass SaveToFileComponent(Component):\n    display_name = \"Write File\"\n    description = \"Save content to a file in the specified format and return its path.\"\n    documentation: str = \"https://docs.langflow.org/write-file\"\n    icon = \"file-text\"\n    name = \"SaveToFile\"\n\n    # File format options for different storage types\n    LOCAL_DATA_FORMAT_CHOICES = [\"csv\", \"excel\", \"json\", \"markdown\"]\n    LOCAL_MESSAGE_FORMAT_CHOICES = [\"txt\", \"html\", \"json\", \"markdown\"]\n    AWS_FORMAT_CHOICES = [\n        \"txt\",\n        \"json\",\n        \"csv\",\n        \"xml\",\n        \"html\",\n        \"md\",\n        \"yaml\",\n        \"log\",\n        \"tsv\",\n        \"jsonl\",\n        \"parquet\",\n        \"xlsx\",\n        \"zip\",\n    ]\n    GDRIVE_FORMAT_CHOICES = [\"txt\", \"html\", \"json\", \"csv\", \"xlsx\", \"slides\", \"docs\", \"jpg\", \"mp3\"]\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to save the file.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=_get_default_storage_location(),\n            advanced=True,\n        ),\n        # Common inputs\n        DataFrameInput(\n            name=\"input\",\n            display_name=\"File Content\",\n            info=(\n                \"The content to save. Accepts a DataFrame, Data, or Message object directly. \"\n                'Can also accept a JSON string (e.g. \\'[{\"col1\": \"val1\"}]\\') which will be '\n                \"parsed into a DataFrame, or plain text which will be saved as a Message.\"\n            ),\n            input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\", \"Message\"],\n            required=True,\n            tool_mode=True,\n        ),\n        StrInput(\n            name=\"file_name\",\n            display_name=\"File Name\",\n            info=\"File name without extension (e.g. 'report'). Extension is added automatically.\",\n            required=True,\n            show=True,\n            tool_mode=True,\n        ),\n        StrInput(\n            name=\"file_format\",\n            display_name=\"File Format (Tool)\",\n            info=\"Output format: 'csv', 'json', 'txt', 'html', 'excel', 'markdown'. Overrides pre-configured format.\",\n            required=False,\n            show=False,\n            tool_mode=True,\n        ),\n        BoolInput(\n            name=\"append_mode\",\n            display_name=\"Append\",\n            info=(\n                \"Append to file if it exists (only for Local storage with plain text formats). \"\n                \"Not supported for cloud storage (AWS/Google Drive).\"\n            ),\n            value=False,\n            show=_is_default_storage(\"Local\"),\n        ),\n        # Format inputs (dynamic based on storage location)\n        DropdownInput(\n            name=\"local_format\",\n            display_name=\"File Format\",\n            options=list(dict.fromkeys(LOCAL_DATA_FORMAT_CHOICES + LOCAL_MESSAGE_FORMAT_CHOICES)),\n            info=\"Select the file format for local storage.\",\n            value=\"json\",\n            show=_is_default_storage(\"Local\"),\n        ),\n        DropdownInput(\n            name=\"aws_format\",\n            display_name=\"File Format\",\n            options=AWS_FORMAT_CHOICES,\n            info=\"Select the file format for AWS S3 storage.\",\n            value=\"txt\",\n            show=_is_default_storage(\"AWS\"),\n        ),\n        DropdownInput(\n            name=\"gdrive_format\",\n            display_name=\"File Format\",\n            options=GDRIVE_FORMAT_CHOICES,\n            info=\"Select the file format for Google Drive storage.\",\n            value=\"txt\",\n            show=_is_default_storage(\"Google Drive\"),\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"Optional. Falls back to the AWS_ACCESS_KEY_ID environment variable.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n            required=False,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"Optional. Falls back to the AWS_SECRET_ACCESS_KEY environment variable.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n            required=False,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Optional. Falls back to the configured object storage bucket.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n            required=False,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n        ),\n        StrInput(\n            name=\"s3_prefix\",\n            display_name=\"S3 Prefix\",\n            info=\"Prefix for all files in S3.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=_is_default_storage(\"Google Drive\"),\n            advanced=not _is_default_storage(\"Google Drive\"),\n            required=True,\n        ),\n        StrInput(\n            name=\"folder_id\",\n            display_name=\"Google Drive Folder ID\",\n            info=(\n                \"The Google Drive folder ID where the file will be uploaded. \"\n                \"The folder must be shared with the service account email.\"\n            ),\n            required=True,\n            show=_is_default_storage(\"Google Drive\"),\n            advanced=not _is_default_storage(\"Google Drive\"),\n        ),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"File Path\",\n            name=\"message\",\n            method=\"save_to_file\",\n            # Tool-facing documentation: ``build_description`` prefers output\n            # ``info`` over the component description when this component is\n            # exposed as an agent tool, so the argument reference lives here\n            # instead of bloating the UI card description.\n            info=(\n                \"Save data to a file. \"\n                \"Arguments: 'input' — the content to save (pass a DataFrame directly, or a JSON string \"\n                \"for tabular data, or plain text for messages); \"\n                \"'file_name' — the name to save as, without extension (e.g. 'report'); \"\n                \"'file_format' — output format: 'csv', 'json', 'txt', 'html', 'excel', 'markdown' (optional). \"\n                \"Returns a confirmation with the file path or URL.\"\n            ),\n        )\n    ]\n\n    def update_build_config(self, build_config, field_value, field_name=None):\n        \"\"\"Update build configuration to show/hide fields based on storage location selection.\"\"\"\n        # Update options dynamically based on cloud environment\n        # This ensures options are refreshed when build_config is updated\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # When tool_mode is toggled, hide storage-specific format dropdowns\n        # (the agent uses the unified file_format input instead)\n        if field_name == \"tool_mode\":\n            format_fields = [\"local_format\", \"aws_format\", \"gdrive_format\"]\n            for f_name in format_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = not bool(field_value)\n            return build_config\n\n        if field_name != \"storage_location\":\n            return build_config\n\n        # Extract selected storage location\n        selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n        # Hide all dynamic fields first\n        dynamic_fields = [\n            \"file_name\",  # Common fields (input is always visible)\n            \"append_mode\",\n            \"local_format\",\n            \"aws_format\",\n            \"gdrive_format\",\n            \"aws_access_key_id\",\n            \"aws_secret_access_key\",\n            \"bucket_name\",\n            \"aws_region\",\n            \"s3_prefix\",\n            \"service_account_key\",\n            \"folder_id\",\n        ]\n\n        for f_name in dynamic_fields:\n            if f_name in build_config:\n                build_config[f_name][\"show\"] = False\n\n        # Show fields based on selected storage location\n        is_tool_mode = build_config.get(\"tools_metadata\", {}).get(\"show\", False)\n\n        if len(selected) == 1:\n            location = selected[0]\n\n            # Show file_name when any storage location is selected\n            if \"file_name\" in build_config:\n                build_config[\"file_name\"][\"show\"] = True\n\n            # Show append_mode only for Local storage (not supported for cloud storage)\n            if \"append_mode\" in build_config:\n                build_config[\"append_mode\"][\"show\"] = location == \"Local\"\n\n            if location == \"Local\":\n                if \"local_format\" in build_config:\n                    build_config[\"local_format\"][\"show\"] = not is_tool_mode\n\n            elif location == \"AWS\":\n                aws_fields = [\n                    \"aws_format\",\n                    \"aws_access_key_id\",\n                    \"aws_secret_access_key\",\n                    \"bucket_name\",\n                    \"aws_region\",\n                    \"s3_prefix\",\n                ]\n                for f_name in aws_fields:\n                    if f_name in build_config:\n                        show = f_name != \"aws_format\" or not is_tool_mode\n                        build_config[f_name][\"show\"] = show\n                        build_config[f_name][\"advanced\"] = False\n\n            elif location == \"Google Drive\":\n                gdrive_fields = [\"gdrive_format\", \"service_account_key\", \"folder_id\"]\n                for f_name in gdrive_fields:\n                    if f_name in build_config:\n                        show = f_name != \"gdrive_format\" or not is_tool_mode\n                        build_config[f_name][\"show\"] = show\n                        build_config[f_name][\"advanced\"] = False\n\n        return build_config\n\n    async def save_to_file(self) -> Message:\n        \"\"\"Save the input to a file and upload it, returning a confirmation message.\"\"\"\n        # Validate inputs\n        if not self.file_name:\n            msg = \"File name must be provided.\"\n            raise ValueError(msg)\n        if not self._get_input_type():\n            msg = \"Input type is not set.\"\n            raise ValueError(msg)\n\n        # Get selected storage location\n        storage_location = self._get_selected_storage_location()\n        if not storage_location:\n            msg = \"Storage location must be selected.\"\n            raise ValueError(msg)\n\n        # Check if Local storage is disabled in cloud environment\n        if storage_location == \"Local\" and is_astra_cloud_environment():\n            msg = \"Local storage is not available in cloud environment. Please use AWS or Google Drive.\"\n            raise ValueError(msg)\n\n        # Route to appropriate save method based on storage location\n        if storage_location == \"Local\":\n            return await self._save_to_local()\n        if storage_location == \"AWS\":\n            return await self._save_to_aws()\n        if storage_location == \"Google Drive\":\n            return await self._save_to_google_drive()\n        msg = f\"Unsupported storage location: {storage_location}\"\n        raise ValueError(msg)\n\n    def _get_input_type(self) -> str:\n        \"\"\"Determine the input type based on the provided input.\"\"\"\n        # Use exact type checking (type() is) instead of isinstance() to avoid inheritance issues.\n        # Since Message inherits from Data, isinstance(message, Data) would return True for Message objects,\n        # causing Message inputs to be incorrectly identified as Data type.\n        if type(self.input) is DataFrame:\n            return \"DataFrame\"\n        if type(self.input) is Message:\n            return \"Message\"\n        if type(self.input) is Data:\n            return \"Data\"\n        # When invoked by a code agent (e.g. OpenDsStar), the input may be a raw\n        # pandas DataFrame rather than OpenXFlow's DataFrame wrapper.\n        if isinstance(self.input, pd.DataFrame):\n            self.input = DataFrame(self.input)\n            return \"DataFrame\"\n        # When invoked as a tool, the agent passes a string. Try to parse it as\n        # tabular JSON (list of objects) → DataFrame, otherwise wrap as Message.\n        if isinstance(self.input, str):\n            self.input = self._coerce_string_input(self.input)\n            return self._get_input_type()\n        msg = f\"Unsupported input type: {type(self.input)}\"\n        raise ValueError(msg)\n\n    def _coerce_string_input(self, value: str) -> DataFrame | Message:\n        \"\"\"Convert a raw string (from agent tool call) into a DataFrame or Message.\n\n        Tries to parse as JSON first — a list of objects or a single object becomes\n        a DataFrame. Anything else is wrapped in a Message.\n        \"\"\"\n        try:\n            parsed = json.loads(value)\n            if isinstance(parsed, list) and parsed and isinstance(parsed[0], dict):\n                return DataFrame(pd.DataFrame(parsed))\n            if isinstance(parsed, dict):\n                return DataFrame(pd.DataFrame([parsed]))\n        except (json.JSONDecodeError, ValueError):\n            pass\n        return Message(text=value)\n\n    def _get_default_format(self) -> str:\n        \"\"\"Return the default file format based on input type.\"\"\"\n        if self._get_input_type() == \"DataFrame\":\n            return \"csv\"\n        if self._get_input_type() == \"Data\":\n            return \"json\"\n        if self._get_input_type() == \"Message\":\n            return \"json\"\n        return \"json\"  # Fallback\n\n    def _adjust_file_path_with_format(self, path: Path, fmt: str) -> Path:\n        \"\"\"Adjust the file path to include the correct extension.\"\"\"\n        file_extension = path.suffix.lower().lstrip(\".\")\n        if fmt == \"excel\":\n            return Path(f\"{path}.xlsx\").expanduser() if file_extension not in [\"xlsx\", \"xls\"] else path\n        return Path(f\"{path}.{fmt}\").expanduser() if file_extension != fmt else path\n\n    def _get_safe_local_file_name(self) -> str:\n        \"\"\"Return a local file basename, rejecting paths before any file access.\"\"\"\n        file_name = str(self.file_name).strip()\n        if not file_name:\n            msg = \"File name must be provided.\"\n            raise ValueError(msg)\n\n        local_path = PurePath(file_name)\n        windows_path = PureWindowsPath(file_name)\n        path_parts = (*local_path.parts, *windows_path.parts)\n\n        if (\n            \"\\x00\" in file_name\n            or local_path.is_absolute()\n            or windows_path.is_absolute()\n            or windows_path.drive\n            or len(local_path.parts) != 1\n            or len(windows_path.parts) != 1\n            or any(part in {\"\", \".\", \"..\"} for part in path_parts)\n        ):\n            msg = \"Local file name must be a file name only, without paths or parent directory references.\"\n            raise ValueError(msg)\n\n        return file_name\n\n    def _is_plain_text_format(self, fmt: str) -> bool:\n        \"\"\"Check if a file format is plain text (supports appending).\"\"\"\n        plain_text_formats = [\"txt\", \"json\", \"markdown\", \"md\", \"csv\", \"xml\", \"html\", \"yaml\", \"log\", \"tsv\", \"jsonl\"]\n        return fmt.lower() in plain_text_formats\n\n    async def _upload_file(self, file_path: Path) -> Any:\n        \"\"\"Upload the saved file using the upload_user_file service.\n\n        Returns the ``UploadFileResponse`` (with the durable storage ``path`` and\n        ``provider``) so the caller can report the real destination and, for\n        remote backends, discard the local staging copy.\n        \"\"\"\n        from langflow.api.v2.files import upload_user_file\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        # Ensure the file exists\n        if not file_path.exists():\n            msg = f\"File not found: {file_path}\"\n            raise FileNotFoundError(msg)\n\n        # Upload the file - always use append=False because the local file already contains\n        # the correct content (either new or appended locally)\n        with file_path.open(\"rb\") as f:\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for file saving.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n\n                return await upload_user_file(\n                    file=UploadFile(filename=file_path.name, file=f, size=file_path.stat().st_size),\n                    session=db,\n                    current_user=current_user,\n                    storage_service=get_storage_service(),\n                    settings_service=get_settings_service(),\n                    append=False,\n                )\n\n    def _save_dataframe(self, dataframe: DataFrame, path: Path, fmt: str) -> str:\n        \"\"\"Save a DataFrame to the specified file format.\"\"\"\n        append_mode = getattr(self, \"append_mode\", False)\n        should_append = append_mode and path.exists() and self._is_plain_text_format(fmt)\n\n        if fmt == \"csv\":\n            dataframe.to_csv(path, index=False, mode=\"a\" if should_append else \"w\", header=not should_append)\n        elif fmt == \"excel\":\n            dataframe.to_excel(path, index=False, engine=\"openpyxl\")\n        elif fmt == \"json\":\n            if should_append:\n                # Read and parse existing JSON\n                existing_data = []\n                try:\n                    existing_content = path.read_text(encoding=\"utf-8\").strip()\n                    if existing_content:\n                        parsed = json.loads(existing_content)\n                        # Handle case where existing content is a single object\n                        if isinstance(parsed, dict):\n                            existing_data = [parsed]\n                        elif isinstance(parsed, list):\n                            existing_data = parsed\n                except (json.JSONDecodeError, FileNotFoundError):\n                    # Treat parse errors or missing file as empty array\n                    existing_data = []\n\n                # Append new data\n                new_records = json.loads(dataframe.to_json(orient=\"records\"))\n                existing_data.extend(new_records)\n\n                # Write back as a single JSON array\n                path.write_text(json.dumps(existing_data, indent=2), encoding=\"utf-8\")\n            else:\n                dataframe.to_json(path, orient=\"records\", indent=2)\n        elif fmt == \"markdown\":\n            content = dataframe.to_markdown(index=False)\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\\n\" + content, encoding=\"utf-8\")\n            else:\n                path.write_text(content, encoding=\"utf-8\")\n        else:\n            msg = f\"Unsupported DataFrame format: {fmt}\"\n            raise ValueError(msg)\n        action = \"appended to\" if should_append else \"saved successfully as\"\n        return f\"DataFrame {action} '{path}'\"\n\n    def _save_data(self, data: Data, path: Path, fmt: str) -> str:\n        \"\"\"Save a Data object to the specified file format.\"\"\"\n        append_mode = getattr(self, \"append_mode\", False)\n        should_append = append_mode and path.exists() and self._is_plain_text_format(fmt)\n\n        if fmt == \"csv\":\n            pd.DataFrame(data.data).to_csv(\n                path,\n                index=False,\n                mode=\"a\" if should_append else \"w\",\n                header=not should_append,\n            )\n        elif fmt == \"excel\":\n            pd.DataFrame(data.data).to_excel(path, index=False, engine=\"openpyxl\")\n        elif fmt == \"json\":\n            new_data = jsonable_encoder(data.data)\n            if should_append:\n                # Read and parse existing JSON\n                existing_data = []\n                try:\n                    existing_content = path.read_text(encoding=\"utf-8\").strip()\n                    if existing_content:\n                        parsed = json.loads(existing_content)\n                        # Handle case where existing content is a single object\n                        if isinstance(parsed, dict):\n                            existing_data = [parsed]\n                        elif isinstance(parsed, list):\n                            existing_data = parsed\n                except (json.JSONDecodeError, FileNotFoundError):\n                    # Treat parse errors or missing file as empty array\n                    existing_data = []\n\n                # Append new data\n                if isinstance(new_data, list):\n                    existing_data.extend(new_data)\n                else:\n                    existing_data.append(new_data)\n\n                # Write back as a single JSON array\n                path.write_text(json.dumps(existing_data, indent=2), encoding=\"utf-8\")\n            else:\n                content = orjson.dumps(new_data, option=orjson.OPT_INDENT_2).decode(\"utf-8\")\n                path.write_text(content, encoding=\"utf-8\")\n        elif fmt == \"markdown\":\n            content = pd.DataFrame(data.data).to_markdown(index=False)\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\\n\" + content, encoding=\"utf-8\")\n            else:\n                path.write_text(content, encoding=\"utf-8\")\n        else:\n            msg = f\"Unsupported Data format: {fmt}\"\n            raise ValueError(msg)\n        action = \"appended to\" if should_append else \"saved successfully as\"\n        return f\"Data {action} '{path}'\"\n\n    async def _save_message(self, message: Message, path: Path, fmt: str) -> str:\n        \"\"\"Save a Message to the specified file format, handling async iterators.\"\"\"\n        content = \"\"\n        stream = message.text_stream if hasattr(message, \"text_stream\") else None\n        if stream is not None and isinstance(stream, AsyncIterator):\n            async for item in stream:\n                content += str(item) + \" \"\n            content = content.strip()\n        elif stream is not None and isinstance(stream, Iterator):\n            content = \" \".join(str(item) for item in stream)\n        else:\n            content = str(message.text)\n\n        append_mode = getattr(self, \"append_mode\", False)\n        should_append = append_mode and path.exists() and self._is_plain_text_format(fmt)\n\n        if fmt in (\"txt\", \"html\"):\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\" + content, encoding=\"utf-8\")\n            else:\n                path.write_text(content, encoding=\"utf-8\")\n        elif fmt == \"json\":\n            new_message = {\"message\": content}\n            if should_append:\n                # Read and parse existing JSON\n                existing_data = []\n                try:\n                    existing_content = path.read_text(encoding=\"utf-8\").strip()\n                    if existing_content:\n                        parsed = json.loads(existing_content)\n                        # Handle case where existing content is a single object\n                        if isinstance(parsed, dict):\n                            existing_data = [parsed]\n                        elif isinstance(parsed, list):\n                            existing_data = parsed\n                except (json.JSONDecodeError, FileNotFoundError):\n                    # Treat parse errors or missing file as empty array\n                    existing_data = []\n\n                # Append new message\n                existing_data.append(new_message)\n\n                # Write back as a single JSON array\n                path.write_text(json.dumps(existing_data, indent=2), encoding=\"utf-8\")\n            else:\n                path.write_text(json.dumps(new_message, indent=2), encoding=\"utf-8\")\n        elif fmt == \"markdown\":\n            md_content = f\"**Message:**\\n\\n{content}\"\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\\n\" + md_content, encoding=\"utf-8\")\n            else:\n                path.write_text(md_content, encoding=\"utf-8\")\n        else:\n            msg = f\"Unsupported Message format: {fmt}\"\n            raise ValueError(msg)\n        action = \"appended to\" if should_append else \"saved successfully as\"\n        return f\"Message {action} '{path}'\"\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"\"\n\n    def _get_file_format_for_location(self, location: str) -> str:\n        \"\"\"Get the appropriate file format based on storage location.\n\n        If the agent set file_format via tool mode, that takes priority.\n        \"\"\"\n        agent_format = getattr(self, \"file_format\", None)\n        if agent_format:\n            return agent_format\n        if location == \"Local\":\n            return getattr(self, \"local_format\", None) or self._get_default_format()\n        if location == \"AWS\":\n            return getattr(self, \"aws_format\", \"txt\")\n        if location == \"Google Drive\":\n            return getattr(self, \"gdrive_format\", \"txt\")\n        return self._get_default_format()\n\n    def _serving_end_user_segment(self) -> str | None:\n        \"\"\"Filesystem-safe end-user id to namespace saved files under, or ``None``.\n\n        On the serving plane an identified end user owns the files they write during their\n        session, so their id (``graph.end_user_id``) namespaces the save destination instead\n        of the shared service account. Encode the complete identity so distinct ids never\n        share a folder, including on case-insensitive filesystems. ``None`` off / editor / anonymous, so\n        the destination falls back to the SID scope exactly as before (strict BC).\n        \"\"\"\n        end_user = self._serving_end_user_id()\n        if not end_user:\n            return None\n        return end_user_folder_segment(end_user)\n\n    def _serving_end_user_id(self) -> str:\n        graph = getattr(getattr(self, \"_vertex\", None), \"graph\", None)\n        end_user = getattr(graph, \"end_user_id\", None)\n        return str(end_user) if end_user else \"\"\n\n    async def _save_to_local(self) -> Message:\n        \"\"\"Save file to local storage (original functionality).\"\"\"\n        file_format = self._get_file_format_for_location(\"Local\")\n\n        # Validate file format based on input type\n        allowed_formats = (\n            self.LOCAL_MESSAGE_FORMAT_CHOICES if self._get_input_type() == \"Message\" else self.LOCAL_DATA_FORMAT_CHOICES\n        )\n        if file_format not in allowed_formats:\n            msg = f\"Invalid file format '{file_format}' for {self._get_input_type()}. Allowed: {allowed_formats}\"\n            raise ValueError(msg)\n\n        # Prepare file path. file_name is tenant-controlled and this writes to local disk.\n        settings = get_settings_service().settings\n        scope_ids = component_file_access_scopes(self)\n        # Serving plane: an identified end user owns their files, so their encoded id\n        # becomes the namespace folder AND an allowed access scope for enforce_local_file_access.\n        # None off / editor / anonymous -> scope_ids unchanged -> SID namespace as before (BC).\n        end_user_segment = self._serving_end_user_segment()\n        if end_user_segment:\n            scope_ids = (end_user_segment, *scope_ids)\n        file_path = Path(self._get_safe_local_file_name()).expanduser()\n        in_end_user_folder = False\n        if settings.restrict_local_file_access and not file_path.is_absolute():\n            # New files belong to the authenticated user's storage namespace. If no\n            # user/flow scope exists, ``enforce_local_file_access`` below fails closed.\n            scope_root = Path(scope_ids[0]) if scope_ids else Path()\n            file_path = Path(settings.config_dir) / scope_root / file_path\n            in_end_user_folder = end_user_segment is not None\n        file_path = self._adjust_file_path_with_format(file_path, file_format)\n        file_path = enforce_local_file_access(file_path, scope_ids=scope_ids)\n        if in_end_user_folder:\n            # Erasure deletes this folder only for the end users recorded as its owners.\n            record_end_user_folder(\n                Path(settings.config_dir), end_user_segment, self._serving_end_user_id(), exclusive=True\n            )\n        if not file_path.parent.exists():\n            file_path.parent.mkdir(parents=True, exist_ok=True)\n\n        # Save the input to file based on type\n        if self._get_input_type() == \"DataFrame\":\n            confirmation = self._save_dataframe(self.input, file_path, file_format)\n        elif self._get_input_type() == \"Data\":\n            confirmation = self._save_data(self.input, file_path, file_format)\n        elif self._get_input_type() == \"Message\":\n            confirmation = await self._save_message(self.input, file_path, file_format)\n        else:\n            msg = f\"Unsupported input type: {self._get_input_type()}\"\n            raise ValueError(msg)\n\n        # Upload the saved file into the configured storage backend.\n        upload_result = await self._upload_file(file_path)\n\n        # When the backend is remote (e.g. S3) the local file was only a\n        # serialization staging area — the durable copy now lives in the storage\n        # service. Delete the staging file (matching AWS-mode cleanup) and report\n        # the storage destination instead of the misleading local path.\n        # Why: on disposable executor pods the local copy is both redundant and\n        # unreachable by other pods, so leaving it behind leaks a file and makes\n        # the returned path point somewhere that does not durably exist. For local\n        # storage the staged file IS the durable artifact, so it is kept as-is.\n        #\n        # Exception: append_mode accumulates content by reading the persisted local\n        # file across calls (should_append hinges on path.exists()). Deleting it\n        # would make the next append silently fall back to overwrite and lose the\n        # accumulated content — so the staging file is preserved when appending.\n        settings = get_settings_service().settings\n        if settings.storage_type != \"local\":\n            if not getattr(self, \"append_mode\", False):\n                with contextlib.suppress(OSError):\n                    file_path.unlink()\n            destination = upload_result.path if upload_result is not None else file_path.name\n            provider = (\n                upload_result.provider\n                if upload_result is not None and upload_result.provider\n                else settings.storage_type\n            ).upper()\n            action = (\n                \"appended to\"\n                if getattr(self, \"append_mode\", False) and self._is_plain_text_format(file_format)\n                else \"saved successfully as\"\n            )\n            return Message(text=f\"{self._get_input_type()} {action} '{destination}' in {provider} storage\")\n\n        # Local storage: the staged file is the durable artifact — keep it and\n        # report its on-disk path.\n        final_path = Path.cwd() / file_path if not file_path.is_absolute() else file_path\n        return Message(text=f\"{confirmation} at {final_path}\")\n\n    async def _save_to_aws(self) -> Message:\n        \"\"\"Save file to AWS S3 using S3 functionality.\"\"\"\n        import os\n\n        import boto3\n\n        # Get AWS credentials from component inputs or fall back to environment variables\n        aws_access_key_id = getattr(self, \"aws_access_key_id\", None)\n        if aws_access_key_id and hasattr(aws_access_key_id, \"get_secret_value\"):\n            aws_access_key_id = aws_access_key_id.get_secret_value()\n        access_key_from_environment = not aws_access_key_id\n        if access_key_from_environment:\n            aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n\n        aws_secret_access_key = getattr(self, \"aws_secret_access_key\", None)\n        if aws_secret_access_key and hasattr(aws_secret_access_key, \"get_secret_value\"):\n            aws_secret_access_key = aws_secret_access_key.get_secret_value()\n        secret_key_from_environment = not aws_secret_access_key\n        if secret_key_from_environment:\n            aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n\n        bucket_name = getattr(self, \"bucket_name\", None)\n        if not bucket_name:\n            # Try to get from storage service settings\n            settings = get_settings_service().settings\n            bucket_name = settings.object_storage_bucket_name\n\n        # Validate AWS credentials\n        if not aws_access_key_id:\n            msg = (\n                \"AWS Access Key ID is required for S3 storage. Provide it as a component input \"\n                \"or set AWS_ACCESS_KEY_ID environment variable.\"\n            )\n            raise ValueError(msg)\n        if not aws_secret_access_key:\n            msg = (\n                \"AWS Secret Key is required for S3 storage. Provide it as a component input \"\n                \"or set AWS_SECRET_ACCESS_KEY environment variable.\"\n            )\n            raise ValueError(msg)\n        if not bucket_name:\n            msg = (\n                \"S3 Bucket Name is required for S3 storage. Provide it as a component input \"\n                \"or set LANGFLOW_OBJECT_STORAGE_BUCKET_NAME environment variable.\"\n            )\n            raise ValueError(msg)\n\n        # Create S3 client from the resolved component or fallback values\n        client_config: dict[str, Any] = {\n            \"aws_access_key_id\": str(aws_access_key_id),\n            \"aws_secret_access_key\": str(aws_secret_access_key),\n        }\n        if access_key_from_environment and secret_key_from_environment:\n            aws_session_token = os.getenv(\"AWS_SESSION_TOKEN\")\n            if aws_session_token:\n                client_config[\"aws_session_token\"] = aws_session_token\n\n        # Get region from component input, environment variable, or settings\n        aws_region = getattr(self, \"aws_region\", None)\n        if not aws_region:\n            aws_region = os.getenv(\"AWS_DEFAULT_REGION\") or os.getenv(\"AWS_REGION\")\n        if aws_region:\n            client_config[\"region_name\"] = str(aws_region)\n\n        s3_client = boto3.client(\"s3\", **client_config)\n\n        # Extract content\n        content = self._extract_content_for_upload()\n        file_format = self._get_file_format_for_location(\"AWS\")\n\n        # Generate file path. Namespace by user_id so concurrent multi-user runs\n        # writing the same file_name don't overwrite each other — mirrors how the\n        # platform storage service isolates files under \"{user_id}/\". Layout:\n        #   {s3_prefix}/{user_id}/{file_name}.{ext}\n        file_name = f\"{self.file_name}.{file_format}\"\n        path_segments = []\n        if hasattr(self, \"s3_prefix\") and self.s3_prefix:\n            path_segments.append(self.s3_prefix.rstrip(\"/\"))\n        # Serving plane: an identified end user owns their files (encoded id); else the SID.\n        user_id = self._serving_end_user_segment() or self.user_id\n        if user_id and str(user_id) != \"None\":\n            path_segments.append(str(user_id))\n        path_segments.append(file_name)\n        file_path = \"/\".join(path_segments)\n\n        # Create temporary file\n        import tempfile\n\n        with tempfile.NamedTemporaryFile(\n            mode=\"w\", encoding=\"utf-8\", suffix=f\".{file_format}\", delete=False\n        ) as temp_file:\n            temp_file.write(content)\n            temp_file_path = temp_file.name\n\n        try:\n            # Upload to S3\n            s3_client.upload_file(temp_file_path, bucket_name, file_path)\n            s3_url = f\"s3://{bucket_name}/{file_path}\"\n            return Message(text=f\"File successfully uploaded to {s3_url}\")\n        finally:\n            # Clean up temp file\n            if Path(temp_file_path).exists():\n                Path(temp_file_path).unlink()\n\n    async def _save_to_google_drive(self) -> Message:\n        \"\"\"Save file to Google Drive using Google Drive functionality.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaFileUpload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"folder_id\", None):\n            msg = \"Google Drive Folder ID is required for Google Drive storage\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with full drive scope (needed for folder operations)\n        drive_service, credentials = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive\"], return_credentials=True\n        )\n\n        # Extract content and format\n        content = self._extract_content_for_upload()\n        file_format = self._get_file_format_for_location(\"Google Drive\")\n\n        # Handle special Google Drive formats\n        if file_format in [\"slides\", \"docs\"]:\n            return await self._save_to_google_apps(drive_service, credentials, content, file_format)\n\n        # Create temporary file\n        file_path = f\"{self.file_name}.{file_format}\"\n        with tempfile.NamedTemporaryFile(\n            mode=\"w\",\n            encoding=\"utf-8\",\n            suffix=f\".{file_format}\",\n            delete=False,\n        ) as temp_file:\n            temp_file.write(content)\n            temp_file_path = temp_file.name\n\n        try:\n            # Upload to Google Drive\n            # Note: We skip explicit folder verification since it requires broader permissions.\n            # If the folder doesn't exist or isn't accessible, the create() call will fail with a clear error.\n            file_metadata = {\"name\": file_path, \"parents\": [self.folder_id]}\n            media = MediaFileUpload(temp_file_path, resumable=True)\n\n            try:\n                uploaded_file = (\n                    drive_service.files().create(body=file_metadata, media_body=media, fields=\"id\").execute()\n                )\n            except Exception as e:\n                msg = (\n                    f\"Unable to upload file to Google Drive folder '{self.folder_id}'. \"\n                    f\"Error: {e!s}. \"\n                    \"Please ensure: 1) The folder ID is correct, 2) The folder exists, \"\n                    \"3) The service account has been granted access to this folder.\"\n                )\n                raise ValueError(msg) from e\n\n            file_id = uploaded_file.get(\"id\")\n            file_url = f\"https://drive.google.com/file/d/{file_id}/view\"\n            return Message(text=f\"File successfully uploaded to Google Drive: {file_url}\")\n        finally:\n            # Clean up temp file\n            if Path(temp_file_path).exists():\n                Path(temp_file_path).unlink()\n\n    async def _save_to_google_apps(self, drive_service, credentials, content: str, app_type: str) -> Message:\n        \"\"\"Save content to Google Apps (Slides or Docs).\"\"\"\n        import time\n\n        if app_type == \"slides\":\n            from googleapiclient.discovery import build\n\n            slides_service = build(\"slides\", \"v1\", credentials=credentials)\n\n            file_metadata = {\n                \"name\": self.file_name,\n                \"mimeType\": \"application/vnd.google-apps.presentation\",\n                \"parents\": [self.folder_id],\n            }\n\n            created_file = drive_service.files().create(body=file_metadata, fields=\"id\").execute()\n            presentation_id = created_file[\"id\"]\n\n            time.sleep(2)  # Wait for file to be available  # noqa: ASYNC251\n\n            presentation = slides_service.presentations().get(presentationId=presentation_id).execute()\n            slide_id = presentation[\"slides\"][0][\"objectId\"]\n\n            # Add content to slide\n            requests = [\n                {\n                    \"createShape\": {\n                        \"objectId\": \"TextBox_01\",\n                        \"shapeType\": \"TEXT_BOX\",\n                        \"elementProperties\": {\n                            \"pageObjectId\": slide_id,\n                            \"size\": {\n                                \"height\": {\"magnitude\": 3000000, \"unit\": \"EMU\"},\n                                \"width\": {\"magnitude\": 6000000, \"unit\": \"EMU\"},\n                            },\n                            \"transform\": {\n                                \"scaleX\": 1,\n                                \"scaleY\": 1,\n                                \"translateX\": 1000000,\n                                \"translateY\": 1000000,\n                                \"unit\": \"EMU\",\n                            },\n                        },\n                    }\n                },\n                {\"insertText\": {\"objectId\": \"TextBox_01\", \"insertionIndex\": 0, \"text\": content}},\n            ]\n\n            slides_service.presentations().batchUpdate(\n                presentationId=presentation_id, body={\"requests\": requests}\n            ).execute()\n            file_url = f\"https://docs.google.com/presentation/d/{presentation_id}/edit\"\n\n        elif app_type == \"docs\":\n            from googleapiclient.discovery import build\n\n            docs_service = build(\"docs\", \"v1\", credentials=credentials)\n\n            file_metadata = {\n                \"name\": self.file_name,\n                \"mimeType\": \"application/vnd.google-apps.document\",\n                \"parents\": [self.folder_id],\n            }\n\n            created_file = drive_service.files().create(body=file_metadata, fields=\"id\").execute()\n            document_id = created_file[\"id\"]\n\n            time.sleep(2)  # Wait for file to be available  # noqa: ASYNC251\n\n            # Add content to document\n            requests = [{\"insertText\": {\"location\": {\"index\": 1}, \"text\": content}}]\n            docs_service.documents().batchUpdate(documentId=document_id, body={\"requests\": requests}).execute()\n            file_url = f\"https://docs.google.com/document/d/{document_id}/edit\"\n\n        return Message(text=f\"File successfully created in Google {app_type.title()}: {file_url}\")\n\n    def _extract_content_for_upload(self) -> str:\n        \"\"\"Extract content from input for upload to cloud services.\"\"\"\n        if self._get_input_type() == \"DataFrame\":\n            return self.input.to_csv(index=False)\n        if self._get_input_type() == \"Data\":\n            if hasattr(self.input, \"data\") and self.input.data:\n                if isinstance(self.input.data, dict):\n                    import json\n\n                    return json.dumps(self.input.data, indent=2, ensure_ascii=False)\n                return str(self.input.data)\n            return str(self.input)\n        if self._get_input_type() == \"Message\":\n            return str(self.input.text) if self.input.text else str(self.input)\n        return str(self.input)\n"
+                "value": "import contextlib\nimport json\nfrom collections.abc import AsyncIterator, Iterator\nfrom pathlib import Path, PurePath, PureWindowsPath\nfrom typing import Any\n\nimport orjson\nimport pandas as pd\nfrom fastapi import UploadFile\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.custom import Component\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DataFrameInput\nfrom lfx.io import BoolInput, DropdownInput, SecretStrInput, StrInput\nfrom lfx.schema import Data, DataFrame, Message\nfrom lfx.services.deps import get_settings_service, get_storage_service, session_scope\nfrom lfx.template.field.base import Output\nfrom lfx.utils.end_user_storage import end_user_folder_segment, record_end_user_folder\nfrom lfx.utils.file_path_security import component_file_access_scopes, enforce_local_file_access\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\ndef _get_default_storage_location() -> list[dict[str, str]]:\n    \"\"\"Return the default storage selection for the component template.\"\"\"\n    return [_get_storage_location_options()[0]]\n\n\ndef _is_default_storage(storage_name: str) -> bool:\n    \"\"\"Check whether a storage type is the default selection.\"\"\"\n    return _get_default_storage_location()[0][\"name\"] == storage_name\n\n\nclass SaveToFileComponent(Component):\n    display_name = \"Write File\"\n    description = \"Save content to a file in the specified format and return its path.\"\n    documentation: str = \"https://docs.langflow.org/write-file\"\n    icon = \"file-text\"\n    name = \"SaveToFile\"\n\n    # File format options for different storage types\n    LOCAL_DATA_FORMAT_CHOICES = [\"csv\", \"excel\", \"json\", \"markdown\"]\n    LOCAL_MESSAGE_FORMAT_CHOICES = [\"txt\", \"html\", \"json\", \"markdown\"]\n    AWS_FORMAT_CHOICES = [\n        \"txt\",\n        \"json\",\n        \"csv\",\n        \"xml\",\n        \"html\",\n        \"md\",\n        \"yaml\",\n        \"log\",\n        \"tsv\",\n        \"jsonl\",\n        \"parquet\",\n        \"xlsx\",\n        \"zip\",\n    ]\n    GDRIVE_FORMAT_CHOICES = [\"txt\", \"html\", \"json\", \"csv\", \"xlsx\", \"slides\", \"docs\", \"jpg\", \"mp3\"]\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to save the file.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=_get_default_storage_location(),\n            advanced=True,\n        ),\n        # Common inputs\n        DataFrameInput(\n            name=\"input\",\n            display_name=\"File Content\",\n            info=(\n                \"The content to save. Accepts a DataFrame, Data, or Message object directly. \"\n                'Can also accept a JSON string (e.g. \\'[{\"col1\": \"val1\"}]\\') which will be '\n                \"parsed into a DataFrame, or plain text which will be saved as a Message.\"\n            ),\n            input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\", \"Message\"],\n            required=True,\n            tool_mode=True,\n        ),\n        StrInput(\n            name=\"file_name\",\n            display_name=\"File Name\",\n            info=\"File name without extension (e.g. 'report'). Extension is added automatically.\",\n            required=True,\n            show=True,\n            tool_mode=True,\n        ),\n        StrInput(\n            name=\"file_format\",\n            display_name=\"File Format (Tool)\",\n            info=\"Output format: 'csv', 'json', 'txt', 'html', 'excel', 'markdown'. Overrides pre-configured format.\",\n            required=False,\n            show=False,\n            tool_mode=True,\n        ),\n        BoolInput(\n            name=\"append_mode\",\n            display_name=\"Append\",\n            info=(\n                \"Append to file if it exists (only for Local storage with plain text formats). \"\n                \"Not supported for cloud storage (AWS/Google Drive).\"\n            ),\n            value=False,\n            show=_is_default_storage(\"Local\"),\n        ),\n        # Format inputs (dynamic based on storage location)\n        DropdownInput(\n            name=\"local_format\",\n            display_name=\"File Format\",\n            options=list(dict.fromkeys(LOCAL_DATA_FORMAT_CHOICES + LOCAL_MESSAGE_FORMAT_CHOICES)),\n            info=\"Select the file format for local storage.\",\n            value=\"json\",\n            show=_is_default_storage(\"Local\"),\n        ),\n        DropdownInput(\n            name=\"aws_format\",\n            display_name=\"File Format\",\n            options=AWS_FORMAT_CHOICES,\n            info=\"Select the file format for AWS S3 storage.\",\n            value=\"txt\",\n            show=_is_default_storage(\"AWS\"),\n        ),\n        DropdownInput(\n            name=\"gdrive_format\",\n            display_name=\"File Format\",\n            options=GDRIVE_FORMAT_CHOICES,\n            info=\"Select the file format for Google Drive storage.\",\n            value=\"txt\",\n            show=_is_default_storage(\"Google Drive\"),\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"Optional. Falls back to the AWS_ACCESS_KEY_ID environment variable.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n            required=False,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"Optional. Falls back to the AWS_SECRET_ACCESS_KEY environment variable.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n            required=False,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Optional. Falls back to the configured object storage bucket.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n            required=False,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n        ),\n        StrInput(\n            name=\"s3_prefix\",\n            display_name=\"S3 Prefix\",\n            info=\"Prefix for all files in S3.\",\n            show=_is_default_storage(\"AWS\"),\n            advanced=not _is_default_storage(\"AWS\"),\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=_is_default_storage(\"Google Drive\"),\n            advanced=not _is_default_storage(\"Google Drive\"),\n            required=True,\n        ),\n        StrInput(\n            name=\"folder_id\",\n            display_name=\"Google Drive Folder ID\",\n            info=(\n                \"The Google Drive folder ID where the file will be uploaded. \"\n                \"The folder must be shared with the service account email.\"\n            ),\n            required=True,\n            show=_is_default_storage(\"Google Drive\"),\n            advanced=not _is_default_storage(\"Google Drive\"),\n        ),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"File Path\",\n            name=\"message\",\n            method=\"save_to_file\",\n            # Tool-facing documentation: ``build_description`` prefers output\n            # ``info`` over the component description when this component is\n            # exposed as an agent tool, so the argument reference lives here\n            # instead of bloating the UI card description.\n            info=(\n                \"Save data to a file. \"\n                \"Arguments: 'input' — the content to save (pass a DataFrame directly, or a JSON string \"\n                \"for tabular data, or plain text for messages); \"\n                \"'file_name' — the name to save as, without extension (e.g. 'report'); \"\n                \"'file_format' — output format: 'csv', 'json', 'txt', 'html', 'excel', 'markdown' (optional). \"\n                \"Returns a confirmation with the file path or URL.\"\n            ),\n        )\n    ]\n\n    def update_build_config(self, build_config, field_value, field_name=None):\n        \"\"\"Update build configuration to show/hide fields based on storage location selection.\"\"\"\n        # Update options dynamically based on cloud environment\n        # This ensures options are refreshed when build_config is updated\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # When tool_mode is toggled, hide storage-specific format dropdowns\n        # (the agent uses the unified file_format input instead)\n        if field_name == \"tool_mode\":\n            format_fields = [\"local_format\", \"aws_format\", \"gdrive_format\"]\n            for f_name in format_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = not bool(field_value)\n            return build_config\n\n        if field_name != \"storage_location\":\n            return build_config\n\n        # Extract selected storage location\n        selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n        # Hide all dynamic fields first\n        dynamic_fields = [\n            \"file_name\",  # Common fields (input is always visible)\n            \"append_mode\",\n            \"local_format\",\n            \"aws_format\",\n            \"gdrive_format\",\n            \"aws_access_key_id\",\n            \"aws_secret_access_key\",\n            \"bucket_name\",\n            \"aws_region\",\n            \"s3_prefix\",\n            \"service_account_key\",\n            \"folder_id\",\n        ]\n\n        for f_name in dynamic_fields:\n            if f_name in build_config:\n                build_config[f_name][\"show\"] = False\n\n        # Show fields based on selected storage location\n        is_tool_mode = build_config.get(\"tools_metadata\", {}).get(\"show\", False)\n\n        if len(selected) == 1:\n            location = selected[0]\n\n            # Show file_name when any storage location is selected\n            if \"file_name\" in build_config:\n                build_config[\"file_name\"][\"show\"] = True\n\n            # Show append_mode only for Local storage (not supported for cloud storage)\n            if \"append_mode\" in build_config:\n                build_config[\"append_mode\"][\"show\"] = location == \"Local\"\n\n            if location == \"Local\":\n                if \"local_format\" in build_config:\n                    build_config[\"local_format\"][\"show\"] = not is_tool_mode\n\n            elif location == \"AWS\":\n                aws_fields = [\n                    \"aws_format\",\n                    \"aws_access_key_id\",\n                    \"aws_secret_access_key\",\n                    \"bucket_name\",\n                    \"aws_region\",\n                    \"s3_prefix\",\n                ]\n                for f_name in aws_fields:\n                    if f_name in build_config:\n                        show = f_name != \"aws_format\" or not is_tool_mode\n                        build_config[f_name][\"show\"] = show\n                        build_config[f_name][\"advanced\"] = False\n\n            elif location == \"Google Drive\":\n                gdrive_fields = [\"gdrive_format\", \"service_account_key\", \"folder_id\"]\n                for f_name in gdrive_fields:\n                    if f_name in build_config:\n                        show = f_name != \"gdrive_format\" or not is_tool_mode\n                        build_config[f_name][\"show\"] = show\n                        build_config[f_name][\"advanced\"] = False\n\n        return build_config\n\n    async def save_to_file(self) -> Message:\n        \"\"\"Save the input to a file and upload it, returning a confirmation message.\"\"\"\n        # Validate inputs\n        if not self.file_name:\n            msg = \"File name must be provided.\"\n            raise ValueError(msg)\n        if not self._get_input_type():\n            msg = \"Input type is not set.\"\n            raise ValueError(msg)\n\n        # Get selected storage location\n        storage_location = self._get_selected_storage_location()\n        if not storage_location:\n            msg = \"Storage location must be selected.\"\n            raise ValueError(msg)\n\n        # Check if Local storage is disabled in cloud environment\n        if storage_location == \"Local\" and is_astra_cloud_environment():\n            msg = \"Local storage is not available in cloud environment. Please use AWS or Google Drive.\"\n            raise ValueError(msg)\n\n        # Route to appropriate save method based on storage location\n        if storage_location == \"Local\":\n            return await self._save_to_local()\n        if storage_location == \"AWS\":\n            return await self._save_to_aws()\n        if storage_location == \"Google Drive\":\n            return await self._save_to_google_drive()\n        msg = f\"Unsupported storage location: {storage_location}\"\n        raise ValueError(msg)\n\n    def _get_input_type(self) -> str:\n        \"\"\"Determine the input type based on the provided input.\"\"\"\n        # Use exact type checking (type() is) instead of isinstance() to avoid inheritance issues.\n        # Since Message inherits from Data, isinstance(message, Data) would return True for Message objects,\n        # causing Message inputs to be incorrectly identified as Data type.\n        if type(self.input) is DataFrame:\n            return \"DataFrame\"\n        if type(self.input) is Message:\n            return \"Message\"\n        if type(self.input) is Data:\n            return \"Data\"\n        # When invoked by a code agent (e.g. OpenDsStar), the input may be a raw\n        # pandas DataFrame rather than Langflow's DataFrame wrapper.\n        if isinstance(self.input, pd.DataFrame):\n            self.input = DataFrame(self.input)\n            return \"DataFrame\"\n        # When invoked as a tool, the agent passes a string. Try to parse it as\n        # tabular JSON (list of objects) → DataFrame, otherwise wrap as Message.\n        if isinstance(self.input, str):\n            self.input = self._coerce_string_input(self.input)\n            return self._get_input_type()\n        msg = f\"Unsupported input type: {type(self.input)}\"\n        raise ValueError(msg)\n\n    def _coerce_string_input(self, value: str) -> DataFrame | Message:\n        \"\"\"Convert a raw string (from agent tool call) into a DataFrame or Message.\n\n        Tries to parse as JSON first — a list of objects or a single object becomes\n        a DataFrame. Anything else is wrapped in a Message.\n        \"\"\"\n        try:\n            parsed = json.loads(value)\n            if isinstance(parsed, list) and parsed and isinstance(parsed[0], dict):\n                return DataFrame(pd.DataFrame(parsed))\n            if isinstance(parsed, dict):\n                return DataFrame(pd.DataFrame([parsed]))\n        except (json.JSONDecodeError, ValueError):\n            pass\n        return Message(text=value)\n\n    def _get_default_format(self) -> str:\n        \"\"\"Return the default file format based on input type.\"\"\"\n        if self._get_input_type() == \"DataFrame\":\n            return \"csv\"\n        if self._get_input_type() == \"Data\":\n            return \"json\"\n        if self._get_input_type() == \"Message\":\n            return \"json\"\n        return \"json\"  # Fallback\n\n    def _adjust_file_path_with_format(self, path: Path, fmt: str) -> Path:\n        \"\"\"Adjust the file path to include the correct extension.\"\"\"\n        file_extension = path.suffix.lower().lstrip(\".\")\n        if fmt == \"excel\":\n            return Path(f\"{path}.xlsx\").expanduser() if file_extension not in [\"xlsx\", \"xls\"] else path\n        return Path(f\"{path}.{fmt}\").expanduser() if file_extension != fmt else path\n\n    def _get_safe_local_file_name(self) -> str:\n        \"\"\"Return a local file basename, rejecting paths before any file access.\"\"\"\n        file_name = str(self.file_name).strip()\n        if not file_name:\n            msg = \"File name must be provided.\"\n            raise ValueError(msg)\n\n        local_path = PurePath(file_name)\n        windows_path = PureWindowsPath(file_name)\n        path_parts = (*local_path.parts, *windows_path.parts)\n\n        if (\n            \"\\x00\" in file_name\n            or local_path.is_absolute()\n            or windows_path.is_absolute()\n            or windows_path.drive\n            or len(local_path.parts) != 1\n            or len(windows_path.parts) != 1\n            or any(part in {\"\", \".\", \"..\"} for part in path_parts)\n        ):\n            msg = \"Local file name must be a file name only, without paths or parent directory references.\"\n            raise ValueError(msg)\n\n        return file_name\n\n    def _is_plain_text_format(self, fmt: str) -> bool:\n        \"\"\"Check if a file format is plain text (supports appending).\"\"\"\n        plain_text_formats = [\"txt\", \"json\", \"markdown\", \"md\", \"csv\", \"xml\", \"html\", \"yaml\", \"log\", \"tsv\", \"jsonl\"]\n        return fmt.lower() in plain_text_formats\n\n    async def _upload_file(self, file_path: Path) -> Any:\n        \"\"\"Upload the saved file using the upload_user_file service.\n\n        Returns the ``UploadFileResponse`` (with the durable storage ``path`` and\n        ``provider``) so the caller can report the real destination and, for\n        remote backends, discard the local staging copy.\n        \"\"\"\n        from langflow.api.v2.files import upload_user_file\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        # Ensure the file exists\n        if not file_path.exists():\n            msg = f\"File not found: {file_path}\"\n            raise FileNotFoundError(msg)\n\n        # Upload the file - always use append=False because the local file already contains\n        # the correct content (either new or appended locally)\n        with file_path.open(\"rb\") as f:\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for file saving.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n\n                return await upload_user_file(\n                    file=UploadFile(filename=file_path.name, file=f, size=file_path.stat().st_size),\n                    session=db,\n                    current_user=current_user,\n                    storage_service=get_storage_service(),\n                    settings_service=get_settings_service(),\n                    append=False,\n                )\n\n    def _save_dataframe(self, dataframe: DataFrame, path: Path, fmt: str) -> str:\n        \"\"\"Save a DataFrame to the specified file format.\"\"\"\n        append_mode = getattr(self, \"append_mode\", False)\n        should_append = append_mode and path.exists() and self._is_plain_text_format(fmt)\n\n        if fmt == \"csv\":\n            dataframe.to_csv(path, index=False, mode=\"a\" if should_append else \"w\", header=not should_append)\n        elif fmt == \"excel\":\n            dataframe.to_excel(path, index=False, engine=\"openpyxl\")\n        elif fmt == \"json\":\n            if should_append:\n                # Read and parse existing JSON\n                existing_data = []\n                try:\n                    existing_content = path.read_text(encoding=\"utf-8\").strip()\n                    if existing_content:\n                        parsed = json.loads(existing_content)\n                        # Handle case where existing content is a single object\n                        if isinstance(parsed, dict):\n                            existing_data = [parsed]\n                        elif isinstance(parsed, list):\n                            existing_data = parsed\n                except (json.JSONDecodeError, FileNotFoundError):\n                    # Treat parse errors or missing file as empty array\n                    existing_data = []\n\n                # Append new data\n                new_records = json.loads(dataframe.to_json(orient=\"records\"))\n                existing_data.extend(new_records)\n\n                # Write back as a single JSON array\n                path.write_text(json.dumps(existing_data, indent=2), encoding=\"utf-8\")\n            else:\n                dataframe.to_json(path, orient=\"records\", indent=2)\n        elif fmt == \"markdown\":\n            content = dataframe.to_markdown(index=False)\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\\n\" + content, encoding=\"utf-8\")\n            else:\n                path.write_text(content, encoding=\"utf-8\")\n        else:\n            msg = f\"Unsupported DataFrame format: {fmt}\"\n            raise ValueError(msg)\n        action = \"appended to\" if should_append else \"saved successfully as\"\n        return f\"DataFrame {action} '{path}'\"\n\n    def _save_data(self, data: Data, path: Path, fmt: str) -> str:\n        \"\"\"Save a Data object to the specified file format.\"\"\"\n        append_mode = getattr(self, \"append_mode\", False)\n        should_append = append_mode and path.exists() and self._is_plain_text_format(fmt)\n\n        if fmt == \"csv\":\n            pd.DataFrame(data.data).to_csv(\n                path,\n                index=False,\n                mode=\"a\" if should_append else \"w\",\n                header=not should_append,\n            )\n        elif fmt == \"excel\":\n            pd.DataFrame(data.data).to_excel(path, index=False, engine=\"openpyxl\")\n        elif fmt == \"json\":\n            new_data = jsonable_encoder(data.data)\n            if should_append:\n                # Read and parse existing JSON\n                existing_data = []\n                try:\n                    existing_content = path.read_text(encoding=\"utf-8\").strip()\n                    if existing_content:\n                        parsed = json.loads(existing_content)\n                        # Handle case where existing content is a single object\n                        if isinstance(parsed, dict):\n                            existing_data = [parsed]\n                        elif isinstance(parsed, list):\n                            existing_data = parsed\n                except (json.JSONDecodeError, FileNotFoundError):\n                    # Treat parse errors or missing file as empty array\n                    existing_data = []\n\n                # Append new data\n                if isinstance(new_data, list):\n                    existing_data.extend(new_data)\n                else:\n                    existing_data.append(new_data)\n\n                # Write back as a single JSON array\n                path.write_text(json.dumps(existing_data, indent=2), encoding=\"utf-8\")\n            else:\n                content = orjson.dumps(new_data, option=orjson.OPT_INDENT_2).decode(\"utf-8\")\n                path.write_text(content, encoding=\"utf-8\")\n        elif fmt == \"markdown\":\n            content = pd.DataFrame(data.data).to_markdown(index=False)\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\\n\" + content, encoding=\"utf-8\")\n            else:\n                path.write_text(content, encoding=\"utf-8\")\n        else:\n            msg = f\"Unsupported Data format: {fmt}\"\n            raise ValueError(msg)\n        action = \"appended to\" if should_append else \"saved successfully as\"\n        return f\"Data {action} '{path}'\"\n\n    async def _save_message(self, message: Message, path: Path, fmt: str) -> str:\n        \"\"\"Save a Message to the specified file format, handling async iterators.\"\"\"\n        content = \"\"\n        stream = message.text_stream if hasattr(message, \"text_stream\") else None\n        if stream is not None and isinstance(stream, AsyncIterator):\n            async for item in stream:\n                content += str(item) + \" \"\n            content = content.strip()\n        elif stream is not None and isinstance(stream, Iterator):\n            content = \" \".join(str(item) for item in stream)\n        else:\n            content = str(message.text)\n\n        append_mode = getattr(self, \"append_mode\", False)\n        should_append = append_mode and path.exists() and self._is_plain_text_format(fmt)\n\n        if fmt in (\"txt\", \"html\"):\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\" + content, encoding=\"utf-8\")\n            else:\n                path.write_text(content, encoding=\"utf-8\")\n        elif fmt == \"json\":\n            new_message = {\"message\": content}\n            if should_append:\n                # Read and parse existing JSON\n                existing_data = []\n                try:\n                    existing_content = path.read_text(encoding=\"utf-8\").strip()\n                    if existing_content:\n                        parsed = json.loads(existing_content)\n                        # Handle case where existing content is a single object\n                        if isinstance(parsed, dict):\n                            existing_data = [parsed]\n                        elif isinstance(parsed, list):\n                            existing_data = parsed\n                except (json.JSONDecodeError, FileNotFoundError):\n                    # Treat parse errors or missing file as empty array\n                    existing_data = []\n\n                # Append new message\n                existing_data.append(new_message)\n\n                # Write back as a single JSON array\n                path.write_text(json.dumps(existing_data, indent=2), encoding=\"utf-8\")\n            else:\n                path.write_text(json.dumps(new_message, indent=2), encoding=\"utf-8\")\n        elif fmt == \"markdown\":\n            md_content = f\"**Message:**\\n\\n{content}\"\n            if should_append:\n                path.write_text(path.read_text(encoding=\"utf-8\") + \"\\n\\n\" + md_content, encoding=\"utf-8\")\n            else:\n                path.write_text(md_content, encoding=\"utf-8\")\n        else:\n            msg = f\"Unsupported Message format: {fmt}\"\n            raise ValueError(msg)\n        action = \"appended to\" if should_append else \"saved successfully as\"\n        return f\"Message {action} '{path}'\"\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"\"\n\n    def _get_file_format_for_location(self, location: str) -> str:\n        \"\"\"Get the appropriate file format based on storage location.\n\n        If the agent set file_format via tool mode, that takes priority.\n        \"\"\"\n        agent_format = getattr(self, \"file_format\", None)\n        if agent_format:\n            return agent_format\n        if location == \"Local\":\n            return getattr(self, \"local_format\", None) or self._get_default_format()\n        if location == \"AWS\":\n            return getattr(self, \"aws_format\", \"txt\")\n        if location == \"Google Drive\":\n            return getattr(self, \"gdrive_format\", \"txt\")\n        return self._get_default_format()\n\n    def _serving_end_user_segment(self) -> str | None:\n        \"\"\"Filesystem-safe end-user id to namespace saved files under, or ``None``.\n\n        On the serving plane an identified end user owns the files they write during their\n        session, so their id (``graph.end_user_id``) namespaces the save destination instead\n        of the shared service account. Encode the complete identity so distinct ids never\n        share a folder, including on case-insensitive filesystems. ``None`` off / editor / anonymous, so\n        the destination falls back to the SID scope exactly as before (strict BC).\n        \"\"\"\n        end_user = self._serving_end_user_id()\n        if not end_user:\n            return None\n        return end_user_folder_segment(end_user)\n\n    def _serving_end_user_id(self) -> str:\n        graph = getattr(getattr(self, \"_vertex\", None), \"graph\", None)\n        end_user = getattr(graph, \"end_user_id\", None)\n        return str(end_user) if end_user else \"\"\n\n    async def _save_to_local(self) -> Message:\n        \"\"\"Save file to local storage (original functionality).\"\"\"\n        file_format = self._get_file_format_for_location(\"Local\")\n\n        # Validate file format based on input type\n        allowed_formats = (\n            self.LOCAL_MESSAGE_FORMAT_CHOICES if self._get_input_type() == \"Message\" else self.LOCAL_DATA_FORMAT_CHOICES\n        )\n        if file_format not in allowed_formats:\n            msg = f\"Invalid file format '{file_format}' for {self._get_input_type()}. Allowed: {allowed_formats}\"\n            raise ValueError(msg)\n\n        # Prepare file path. file_name is tenant-controlled and this writes to local disk.\n        settings = get_settings_service().settings\n        scope_ids = component_file_access_scopes(self)\n        # Serving plane: an identified end user owns their files, so their encoded id\n        # becomes the namespace folder AND an allowed access scope for enforce_local_file_access.\n        # None off / editor / anonymous -> scope_ids unchanged -> SID namespace as before (BC).\n        end_user_segment = self._serving_end_user_segment()\n        if end_user_segment:\n            scope_ids = (end_user_segment, *scope_ids)\n        file_path = Path(self._get_safe_local_file_name()).expanduser()\n        in_end_user_folder = False\n        if settings.restrict_local_file_access and not file_path.is_absolute():\n            # New files belong to the authenticated user's storage namespace. If no\n            # user/flow scope exists, ``enforce_local_file_access`` below fails closed.\n            scope_root = Path(scope_ids[0]) if scope_ids else Path()\n            file_path = Path(settings.config_dir) / scope_root / file_path\n            in_end_user_folder = end_user_segment is not None\n        file_path = self._adjust_file_path_with_format(file_path, file_format)\n        file_path = enforce_local_file_access(file_path, scope_ids=scope_ids)\n        if in_end_user_folder:\n            # Erasure deletes this folder only for the end users recorded as its owners.\n            record_end_user_folder(\n                Path(settings.config_dir), end_user_segment, self._serving_end_user_id(), exclusive=True\n            )\n        if not file_path.parent.exists():\n            file_path.parent.mkdir(parents=True, exist_ok=True)\n\n        # Save the input to file based on type\n        if self._get_input_type() == \"DataFrame\":\n            confirmation = self._save_dataframe(self.input, file_path, file_format)\n        elif self._get_input_type() == \"Data\":\n            confirmation = self._save_data(self.input, file_path, file_format)\n        elif self._get_input_type() == \"Message\":\n            confirmation = await self._save_message(self.input, file_path, file_format)\n        else:\n            msg = f\"Unsupported input type: {self._get_input_type()}\"\n            raise ValueError(msg)\n\n        # Upload the saved file into the configured storage backend.\n        upload_result = await self._upload_file(file_path)\n\n        # When the backend is remote (e.g. S3) the local file was only a\n        # serialization staging area — the durable copy now lives in the storage\n        # service. Delete the staging file (matching AWS-mode cleanup) and report\n        # the storage destination instead of the misleading local path.\n        # Why: on disposable executor pods the local copy is both redundant and\n        # unreachable by other pods, so leaving it behind leaks a file and makes\n        # the returned path point somewhere that does not durably exist. For local\n        # storage the staged file IS the durable artifact, so it is kept as-is.\n        #\n        # Exception: append_mode accumulates content by reading the persisted local\n        # file across calls (should_append hinges on path.exists()). Deleting it\n        # would make the next append silently fall back to overwrite and lose the\n        # accumulated content — so the staging file is preserved when appending.\n        settings = get_settings_service().settings\n        if settings.storage_type != \"local\":\n            if not getattr(self, \"append_mode\", False):\n                with contextlib.suppress(OSError):\n                    file_path.unlink()\n            destination = upload_result.path if upload_result is not None else file_path.name\n            provider = (\n                upload_result.provider\n                if upload_result is not None and upload_result.provider\n                else settings.storage_type\n            ).upper()\n            action = (\n                \"appended to\"\n                if getattr(self, \"append_mode\", False) and self._is_plain_text_format(file_format)\n                else \"saved successfully as\"\n            )\n            return Message(text=f\"{self._get_input_type()} {action} '{destination}' in {provider} storage\")\n\n        # Local storage: the staged file is the durable artifact — keep it and\n        # report its on-disk path.\n        final_path = Path.cwd() / file_path if not file_path.is_absolute() else file_path\n        return Message(text=f\"{confirmation} at {final_path}\")\n\n    async def _save_to_aws(self) -> Message:\n        \"\"\"Save file to AWS S3 using S3 functionality.\"\"\"\n        import os\n\n        import boto3\n\n        # Get AWS credentials from component inputs or fall back to environment variables\n        aws_access_key_id = getattr(self, \"aws_access_key_id\", None)\n        if aws_access_key_id and hasattr(aws_access_key_id, \"get_secret_value\"):\n            aws_access_key_id = aws_access_key_id.get_secret_value()\n        access_key_from_environment = not aws_access_key_id\n        if access_key_from_environment:\n            aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n\n        aws_secret_access_key = getattr(self, \"aws_secret_access_key\", None)\n        if aws_secret_access_key and hasattr(aws_secret_access_key, \"get_secret_value\"):\n            aws_secret_access_key = aws_secret_access_key.get_secret_value()\n        secret_key_from_environment = not aws_secret_access_key\n        if secret_key_from_environment:\n            aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n\n        bucket_name = getattr(self, \"bucket_name\", None)\n        if not bucket_name:\n            # Try to get from storage service settings\n            settings = get_settings_service().settings\n            bucket_name = settings.object_storage_bucket_name\n\n        # Validate AWS credentials\n        if not aws_access_key_id:\n            msg = (\n                \"AWS Access Key ID is required for S3 storage. Provide it as a component input \"\n                \"or set AWS_ACCESS_KEY_ID environment variable.\"\n            )\n            raise ValueError(msg)\n        if not aws_secret_access_key:\n            msg = (\n                \"AWS Secret Key is required for S3 storage. Provide it as a component input \"\n                \"or set AWS_SECRET_ACCESS_KEY environment variable.\"\n            )\n            raise ValueError(msg)\n        if not bucket_name:\n            msg = (\n                \"S3 Bucket Name is required for S3 storage. Provide it as a component input \"\n                \"or set LANGFLOW_OBJECT_STORAGE_BUCKET_NAME environment variable.\"\n            )\n            raise ValueError(msg)\n\n        # Create S3 client from the resolved component or fallback values\n        client_config: dict[str, Any] = {\n            \"aws_access_key_id\": str(aws_access_key_id),\n            \"aws_secret_access_key\": str(aws_secret_access_key),\n        }\n        if access_key_from_environment and secret_key_from_environment:\n            aws_session_token = os.getenv(\"AWS_SESSION_TOKEN\")\n            if aws_session_token:\n                client_config[\"aws_session_token\"] = aws_session_token\n\n        # Get region from component input, environment variable, or settings\n        aws_region = getattr(self, \"aws_region\", None)\n        if not aws_region:\n            aws_region = os.getenv(\"AWS_DEFAULT_REGION\") or os.getenv(\"AWS_REGION\")\n        if aws_region:\n            client_config[\"region_name\"] = str(aws_region)\n\n        s3_client = boto3.client(\"s3\", **client_config)\n\n        # Extract content\n        content = self._extract_content_for_upload()\n        file_format = self._get_file_format_for_location(\"AWS\")\n\n        # Generate file path. Namespace by user_id so concurrent multi-user runs\n        # writing the same file_name don't overwrite each other — mirrors how the\n        # platform storage service isolates files under \"{user_id}/\". Layout:\n        #   {s3_prefix}/{user_id}/{file_name}.{ext}\n        file_name = f\"{self.file_name}.{file_format}\"\n        path_segments = []\n        if hasattr(self, \"s3_prefix\") and self.s3_prefix:\n            path_segments.append(self.s3_prefix.rstrip(\"/\"))\n        # Serving plane: an identified end user owns their files (encoded id); else the SID.\n        user_id = self._serving_end_user_segment() or self.user_id\n        if user_id and str(user_id) != \"None\":\n            path_segments.append(str(user_id))\n        path_segments.append(file_name)\n        file_path = \"/\".join(path_segments)\n\n        # Create temporary file\n        import tempfile\n\n        with tempfile.NamedTemporaryFile(\n            mode=\"w\", encoding=\"utf-8\", suffix=f\".{file_format}\", delete=False\n        ) as temp_file:\n            temp_file.write(content)\n            temp_file_path = temp_file.name\n\n        try:\n            # Upload to S3\n            s3_client.upload_file(temp_file_path, bucket_name, file_path)\n            s3_url = f\"s3://{bucket_name}/{file_path}\"\n            return Message(text=f\"File successfully uploaded to {s3_url}\")\n        finally:\n            # Clean up temp file\n            if Path(temp_file_path).exists():\n                Path(temp_file_path).unlink()\n\n    async def _save_to_google_drive(self) -> Message:\n        \"\"\"Save file to Google Drive using Google Drive functionality.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaFileUpload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"folder_id\", None):\n            msg = \"Google Drive Folder ID is required for Google Drive storage\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with full drive scope (needed for folder operations)\n        drive_service, credentials = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive\"], return_credentials=True\n        )\n\n        # Extract content and format\n        content = self._extract_content_for_upload()\n        file_format = self._get_file_format_for_location(\"Google Drive\")\n\n        # Handle special Google Drive formats\n        if file_format in [\"slides\", \"docs\"]:\n            return await self._save_to_google_apps(drive_service, credentials, content, file_format)\n\n        # Create temporary file\n        file_path = f\"{self.file_name}.{file_format}\"\n        with tempfile.NamedTemporaryFile(\n            mode=\"w\",\n            encoding=\"utf-8\",\n            suffix=f\".{file_format}\",\n            delete=False,\n        ) as temp_file:\n            temp_file.write(content)\n            temp_file_path = temp_file.name\n\n        media = None\n        try:\n            # Upload to Google Drive\n            # Note: We skip explicit folder verification since it requires broader permissions.\n            # If the folder doesn't exist or isn't accessible, the create() call will fail with a clear error.\n            file_metadata = {\"name\": file_path, \"parents\": [self.folder_id]}\n            media = MediaFileUpload(temp_file_path, resumable=True)\n\n            try:\n                uploaded_file = (\n                    drive_service.files().create(body=file_metadata, media_body=media, fields=\"id\").execute()\n                )\n            except Exception as e:\n                msg = (\n                    f\"Unable to upload file to Google Drive folder '{self.folder_id}'. \"\n                    f\"Error: {e!s}. \"\n                    \"Please ensure: 1) The folder ID is correct, 2) The folder exists, \"\n                    \"3) The service account has been granted access to this folder.\"\n                )\n                raise ValueError(msg) from e\n\n            file_id = uploaded_file.get(\"id\")\n            file_url = f\"https://drive.google.com/file/d/{file_id}/view\"\n            return Message(text=f\"File successfully uploaded to Google Drive: {file_url}\")\n        finally:\n            if media is not None:\n                media.stream().close()\n            # Clean up temp file\n            if Path(temp_file_path).exists():\n                Path(temp_file_path).unlink()\n\n    async def _save_to_google_apps(self, drive_service, credentials, content: str, app_type: str) -> Message:\n        \"\"\"Save content to Google Apps (Slides or Docs).\"\"\"\n        import time\n\n        if app_type == \"slides\":\n            from googleapiclient.discovery import build\n\n            slides_service = build(\"slides\", \"v1\", credentials=credentials)\n\n            file_metadata = {\n                \"name\": self.file_name,\n                \"mimeType\": \"application/vnd.google-apps.presentation\",\n                \"parents\": [self.folder_id],\n            }\n\n            created_file = drive_service.files().create(body=file_metadata, fields=\"id\").execute()\n            presentation_id = created_file[\"id\"]\n\n            time.sleep(2)  # Wait for file to be available  # noqa: ASYNC251\n\n            presentation = slides_service.presentations().get(presentationId=presentation_id).execute()\n            slide_id = presentation[\"slides\"][0][\"objectId\"]\n\n            # Add content to slide\n            requests = [\n                {\n                    \"createShape\": {\n                        \"objectId\": \"TextBox_01\",\n                        \"shapeType\": \"TEXT_BOX\",\n                        \"elementProperties\": {\n                            \"pageObjectId\": slide_id,\n                            \"size\": {\n                                \"height\": {\"magnitude\": 3000000, \"unit\": \"EMU\"},\n                                \"width\": {\"magnitude\": 6000000, \"unit\": \"EMU\"},\n                            },\n                            \"transform\": {\n                                \"scaleX\": 1,\n                                \"scaleY\": 1,\n                                \"translateX\": 1000000,\n                                \"translateY\": 1000000,\n                                \"unit\": \"EMU\",\n                            },\n                        },\n                    }\n                },\n                {\"insertText\": {\"objectId\": \"TextBox_01\", \"insertionIndex\": 0, \"text\": content}},\n            ]\n\n            slides_service.presentations().batchUpdate(\n                presentationId=presentation_id, body={\"requests\": requests}\n            ).execute()\n            file_url = f\"https://docs.google.com/presentation/d/{presentation_id}/edit\"\n\n        elif app_type == \"docs\":\n            from googleapiclient.discovery import build\n\n            docs_service = build(\"docs\", \"v1\", credentials=credentials)\n\n            file_metadata = {\n                \"name\": self.file_name,\n                \"mimeType\": \"application/vnd.google-apps.document\",\n                \"parents\": [self.folder_id],\n            }\n\n            created_file = drive_service.files().create(body=file_metadata, fields=\"id\").execute()\n            document_id = created_file[\"id\"]\n\n            time.sleep(2)  # Wait for file to be available  # noqa: ASYNC251\n\n            # Add content to document\n            requests = [{\"insertText\": {\"location\": {\"index\": 1}, \"text\": content}}]\n            docs_service.documents().batchUpdate(documentId=document_id, body={\"requests\": requests}).execute()\n            file_url = f\"https://docs.google.com/document/d/{document_id}/edit\"\n\n        return Message(text=f\"File successfully created in Google {app_type.title()}: {file_url}\")\n\n    def _extract_content_for_upload(self) -> str:\n        \"\"\"Extract content from input for upload to cloud services.\"\"\"\n        if self._get_input_type() == \"DataFrame\":\n            return self.input.to_csv(index=False)\n        if self._get_input_type() == \"Data\":\n            if hasattr(self.input, \"data\") and self.input.data:\n                if isinstance(self.input.data, dict):\n                    import json\n\n                    return json.dumps(self.input.data, indent=2, ensure_ascii=False)\n                return str(self.input.data)\n            return str(self.input)\n        if self._get_input_type() == \"Message\":\n            return str(self.input.text) if self.input.text else str(self.input)\n        return str(self.input)\n"
               },
               "file_format": {
                 "_input_type": "StrInput",
@@ -3067,4 +3067,4 @@
     "web-scraping",
     "agents"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json
index fbb2a10b68..2edab2e1b8 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json	
@@ -443,11 +443,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -459,13 +460,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -615,7 +615,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1104,7 +1104,8 @@
             "icon": "layout-template",
             "legacy": false,
             "metadata": {
-              "code_hash": "a6429c7f9670",
+              "code_hash": "8052088025a1",
+              "component_class_name": "URLComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1113,21 +1114,20 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
-                  },
-                  {
-                    "name": "markitdown",
-                    "version": "0.1.6"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "lfx",
                     "version": null
+                  },
+                  {
+                    "name": "markitdown",
+                    "version": "0.1.6"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.data_source.url.URLComponent",
-              "component_class_name": "URLComponent"
+              "module": "lfx.components.data_source.url.URLComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1211,7 +1211,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom markitdown import MarkItDown\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies OpenXFlow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
+                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        # markitdown imports magika, which loads the nearest .env at import time. Keep that\n        # optional converter out of component discovery and load it only when Markdown output\n        # is actually requested.\n        from markitdown import MarkItDown\n\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
               },
               "continue_on_failure": {
                 "_input_type": "BoolInput",
@@ -1811,7 +1811,7 @@
             "description": "# 🧩 Custom Component Generator\n\nDescribe the component you want and an **Agent** writes an OpenXFlow custom Component (Python). It pulls OpenXFlow's reference code on demand and remembers the conversation so you can iterate.\n\n## Setup\n1. **Agent** - add your LLM key (any provider in the Model dropdown).\n2. **URL** and **Memory** tools need no key.\n\n## How it works\n1. **Chat Input → Agent** - your component request.\n2. The Agent uses the **URL** tool to fetch OpenXFlow reference code (base class, examples) when it needs the exact conventions, and **Memory** to recall earlier turns.\n3. **Agent → Chat Output** - the generated Python. Ask follow-ups to refine it.\n\n## Run\nOpen **Playground**, describe the component you need, and run.",
             "display_name": "",
             "documentation": "",
-            "i18n_key": "template_notes.custom_component_generator.b1d3ed28",
+            "i18n_key": "template_notes.custom_component_generator.5073eb43",
             "template": {
               "backgroundColor": "blue"
             }
@@ -1867,6 +1867,7 @@
             "legacy": false,
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1875,7 +1876,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -1884,8 +1885,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -2157,4 +2157,4 @@
     "coding",
     "web-scraping"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Deep Research Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Deep Research Agent.json
index f69d3f987c..43b380a7c6 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Deep Research Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Deep Research Agent.json	
@@ -935,6 +935,7 @@
             "lf_version": "1.4.3",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -943,7 +944,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -952,8 +953,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -1229,11 +1229,12 @@
             "legacy": false,
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1245,13 +1246,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1401,7 +1401,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1997,11 +1997,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2013,13 +2014,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2169,7 +2169,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2777,11 +2777,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2793,13 +2794,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2949,7 +2949,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -3545,6 +3545,7 @@
             "legacy": false,
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -3561,7 +3562,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -3574,8 +3575,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -3972,4 +3972,4 @@
     "assistants",
     "agents"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json
index db6d83d37d..d3ba5e8cea 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json	
@@ -695,7 +695,7 @@
             "legacy": false,
             "lf_version": "1.10.0",
             "metadata": {
-              "code_hash": "b41636ff3a48",
+              "code_hash": "53a28a3025de",
               "component_class_name": "KnowledgeComponent",
               "dependencies": {
                 "dependencies": [
@@ -835,7 +835,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Unified Knowledge component — ingest into or retrieve from a knowledge base.\n\nThis component merges what used to live in ``ingestion.py`` and\n``retrieval.py`` into a single, mode-driven component. A ``TabInput``\n(\"📥 Ingest\" / \"🔍 Retrieve\") drives which inputs and which output are\nvisible. The merged shape gives users one node per KB instead of two\nparallel ones that always shared the same ``knowledge_base`` picker,\nembedding-model metadata, and backend registry.\n\nBoth legacy classes (``KnowledgeIngestionComponent``,\n``KnowledgeBaseComponent``) remain importable as thin subclasses of\n``KnowledgeComponent`` — see the sibling ``ingestion.py`` / ``retrieval.py``\nmodules — so saved flows continue to load unchanged.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport uuid\nfrom collections.abc import Awaitable, Mapping\nfrom contextlib import aclosing\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, TypeVar\n\nimport anyio\nimport pandas as pd\n\nfrom lfx.base.knowledge_bases.backends import BackendType, BaseVectorStoreBackend\nfrom lfx.base.knowledge_bases.backends.base import BackendConfigurationError, IngestedDocument\nfrom lfx.base.knowledge_bases.backends.naming import ensure_storage_routing_allowed\nfrom lfx.base.knowledge_bases.ingestion_sources.base import (\n    IngestionItemResult,\n    IngestionItemStatus,\n    IngestionRunStatus,\n    IngestionSummary,\n)\nfrom lfx.base.knowledge_bases.ingestion_sources.flow_component import FlowComponentSource\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.knowledge_bases.validation import (\n    is_valid_collection_name as is_valid_kb_collection_name,\n)\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.helpers.base_model import coalesce_bool\nfrom lfx.io import (\n    BoolInput,\n    DBProviderInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MessageTextInput,\n    ModelInput,\n    Output,\n    SecretStrInput,\n    StrInput,\n    TabInput,\n    TableInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n    session_scope,\n)\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n\ndef _inputs_for_mode(default_mode: str) -> list:\n    \"\"\"Return a fresh copy of the canonical inputs list with show flags set for the given mode.\n\n    Used by the legacy subclasses so a saved flow keyed on\n    ``KnowledgeIngestion`` / ``KnowledgeBase`` lands on a node template\n    whose default visibility already matches the pinned mode — no\n    \"wait for the user to click the mode tab\" UX gap on load.\n    \"\"\"\n    always_visible = set(KnowledgeComponent.default_keys) | set(KnowledgeComponent.mode_config[default_mode])\n    inputs_copy = []\n    for inp in KnowledgeComponent.inputs:\n        clone = inp.model_copy(deep=True) if hasattr(inp, \"model_copy\") else inp\n        if clone.name == \"mode\":\n            clone.value = default_mode\n        else:\n            clone.show = clone.name in always_visible\n        inputs_copy.append(clone)\n    return inputs_copy\n\n\nif TYPE_CHECKING:\n    from pathlib import Path\n\n# Mode constants. Plain-text labels (no emoji) for consistency with the rest of\n# OpenXFlow's TabInput palette — see ``MemoryComponent`` for the same convention.\nMODE_INGEST = \"Ingest\"\nMODE_RETRIEVE = \"Retrieve\"\n\n# How often a flow ingestion checks whether its job was cancelled from the\n# Knowledge page while it waits on the embedding provider.\nJOB_CANCEL_POLL_SECONDS = 1.0\n\n\nclass KnowledgeIngestionCancelledError(Exception):\n    \"\"\"The ingestion's job was cancelled from the Knowledge page.\"\"\"\n\n\n_T = TypeVar(\"_T\")\n\n\nasync def _finish_despite_cancellation(awaitable: Awaitable[_T]) -> _T:\n    \"\"\"Run ``awaitable`` to completion even if the caller is cancelled meanwhile.\n\n    ``anyio.CancelScope(shield=True)`` holds off AnyIO cancellation, but a\n    repeated native ``Task.cancel()`` still interrupts it, and a stopped flow\n    build can cancel its task more than once. The work runs in its own task,\n    and the caller waits through every cancellation, re-raising once the work\n    has landed.\n    \"\"\"\n    task = asyncio.ensure_future(awaitable)\n    cancelled = False\n    with anyio.CancelScope(shield=True):\n        while not task.done():\n            try:\n                await asyncio.wait({task})\n            except asyncio.CancelledError:\n                cancelled = True\n    if cancelled:\n        error = None if task.cancelled() else task.exception()\n        if error is not None:\n            logger.warning(f\"Work finished during cancellation failed: {error!r}\")\n        raise asyncio.CancelledError from error\n    return task.result()\n\n\ndef _is_retrieve_mode(value: Any) -> bool:\n    \"\"\"Lenient mode check: treats any label containing 'Retrieve' as retrieve mode.\n\n    Older saved flows may carry the emoji-prefixed labels (\"📥 Ingest\" /\n    \"🔍 Retrieve\") this component used to ship with; substring matching\n    keeps those loading without forcing a flow rewrite.\n    \"\"\"\n    return isinstance(value, str) and \"Retrieve\" in value\n\n\n# Boolean flags of a ``column_config`` row. Toggled cells hold real booleans,\n# but a typed cell (or a flow saved while the cell rendered as plain text)\n# keeps the raw string, e.g. ``\"true\"`` — read them with ``coalesce_bool``.\n_COLUMN_FLAGS = (\"vectorize\", \"identifier\")\n\n\ndef _normalize_column_config(config_list: list[dict[str, Any]]) -> list[dict[str, Any]]:\n    \"\"\"Return copies of the rows with every flag as a real boolean.\"\"\"\n    return [{**row, **{key: coalesce_bool(row.get(key)) for key in _COLUMN_FLAGS}} for row in config_list]\n\n\n# Error message used by both the ingest and retrieve paths when the user is\n# running against an Astra cloud environment that disables these flows.\nastra_error_msg = \"Knowledge ingestion and retrieval are not supported in Astra cloud environment.\"\n\n_DEFAULT_OPENSEARCH_CONFIG = {\n    \"url_variable\": \"OPENSEARCH_URL\",\n    \"username_variable\": \"OPENSEARCH_USERNAME\",\n    \"password_variable\": \"OPENSEARCH_PASSWORD\",  # pragma: allowlist secret\n    \"index_name\": \"\",\n    \"vector_field\": \"vector_field\",\n    \"text_field\": \"text\",\n}\n\n\nclass KnowledgeComponent(Component):\n    \"\"\"One component for both writing into and reading from an OpenXFlow knowledge base.\n\n    A ``TabInput`` switches between ingestion and retrieval. The\n    ``update_build_config`` / ``update_outputs`` hooks hide the inputs\n    and the output that don't apply to the current mode so the canvas\n    node stays focused.\n    \"\"\"\n\n    display_name = \"Knowledge\"\n    description = \"Ingest into or retrieve from an OpenXFlow knowledge base.\"\n    icon = \"database\"\n    name = \"Knowledge\"\n\n    # ------ Mode → visible-fields wiring ---------------------------------\n    # ``default_keys`` are inputs always visible regardless of mode.\n    # ``mode_config`` lists the inputs unique to each mode; everything outside\n    # this set is hidden when its mode is not selected.\n    default_keys: list[str] = [\"mode\", \"knowledge_base\"]\n    mode_config: dict[str, list[str]] = {\n        MODE_INGEST: [\n            \"input_df\",\n            \"column_config\",\n            \"chunk_size\",\n            \"api_key\",\n            \"allow_duplicates\",\n            \"metadata_json\",\n        ],\n        MODE_RETRIEVE: [\n            \"search_query\",\n            \"top_k\",\n            \"include_metadata\",\n            \"include_embeddings\",\n            \"metadata_filter\",\n        ],\n    }\n\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self._cached_kb_path: Path | None = None\n        self._knowledge_output_result: Data | DataFrame | None = None\n\n    def _pre_run_setup(self) -> None:\n        self._knowledge_output_result = None\n\n    async def _get_output_result(self, output: Output):\n        # Both legacy handles dispatch to the selected mode. Terminal nodes\n        # evaluate both handles, but must execute the storage operation once.\n        if output.method not in {\"build_kb_info\", \"retrieve_data\"}:\n            return await super()._get_output_result(output)\n        if self._knowledge_output_result is None:\n            raw_output = output.model_copy(update={\"options\": None})\n            self._knowledge_output_result = await super()._get_output_result(raw_output)\n        output.value = output.apply_options(self._knowledge_output_result)\n        return output.value\n\n    @staticmethod\n    def _embedding_provider_from_selection(selection: Any) -> str | None:\n        \"\"\"Read a provider name from a raw or persisted model selection.\"\"\"\n        if isinstance(selection, list):\n            selection = selection[0] if selection else None\n        if not isinstance(selection, Mapping):\n            return None\n        provider = selection.get(\"provider\")\n        if not isinstance(provider, str) or not provider.strip():\n            return None\n        return provider.strip()\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Resolve the embedding provider before dialog hooks or runtime secrets.\n\n        New-KB configuration carries its ModelInput inside a dropdown dialog,\n        while existing KBs persist the provider in the KB row. Neither is a\n        top-level ModelInput, so both must be resolved explicitly before the\n        generic component pipeline hydrates ``api_key``.\n        \"\"\"\n        from lfx.base.models.provider_registry import resolve_provider_id\n        from lfx.services.model_provider_policy import ModelProviderPolicyError\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        knowledge_value = effective_parameters.get(\"knowledge_base\", getattr(self, \"knowledge_base\", None))\n\n        if isinstance(knowledge_value, Mapping):\n            if \"02_embedding_model\" not in knowledge_value:\n                return ()\n            provider = self._embedding_provider_from_selection(knowledge_value.get(\"02_embedding_model\"))\n        elif isinstance(knowledge_value, str) and knowledge_value.strip():\n            metadata = await self._get_kb_metadata(knowledge_value.strip())\n            provider = self._embedding_provider_from_selection(metadata.get(\"model_selection\"))\n            if provider is None:\n                legacy_provider = metadata.get(\"embedding_provider\")\n                provider = legacy_provider.strip() if isinstance(legacy_provider, str) else None\n                if provider == \"Unknown\":\n                    provider = None\n        else:\n            return ()\n\n        if provider is None:\n            unknown_provider = \"unknown\"\n            raise ModelProviderPolicyError(unknown_provider, purpose)\n        return (resolve_provider_id(provider),)\n\n    @dataclass\n    class NewKnowledgeBaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"name\": \"create_knowledge_base\",\n                        \"description\": \"Create new knowledge in OpenXFlow.\",\n                        \"display_name\": \"Create new Knowledge Base\",\n                        \"field_order\": [\n                            \"01_new_kb_name\",\n                            \"02_embedding_model\",\n                            \"03_knowledge_backend\",\n                        ],\n                        \"template\": {\n                            \"01_new_kb_name\": StrInput(\n                                name=\"new_kb_name\",\n                                display_name=\"Knowledge Name\",\n                                info=\"Name of the new knowledge to create.\",\n                                required=True,\n                            ),\n                            \"02_embedding_model\": ModelInput(\n                                name=\"embedding_model\",\n                                display_name=\"Choose Embedding Model\",\n                                info=(\n                                    \"Select the embedding model to use for this knowledge base. \"\n                                    \"OpenXFlow uses the configured credentials for that model provider.\"\n                                ),\n                                required=True,\n                                model_type=\"embedding\",\n                            ),\n                            \"03_knowledge_backend\": DBProviderInput(\n                                name=\"knowledge_backend\",\n                                display_name=\"DB Provider\",\n                                info=(\n                                    \"Select where this knowledge base stores vectors. \"\n                                    \"OpenSearch uses the global DB Providers settings.\"\n                                ),\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    # ------ Inputs --------------------------------------------------------\n    # ``knowledge_base`` is kept at position 0 so legacy tests that check\n    # ``component.inputs[0].dialog_inputs`` continue to work.\n    inputs = [\n        DropdownInput(\n            name=\"knowledge_base\",\n            display_name=\"Knowledge\",\n            info=\"Select the knowledge to load data from.\",\n            required=True,\n            options=[],\n            refresh_button=True,\n            real_time_refresh=True,\n            dialog_inputs=asdict(NewKnowledgeBaseInput()),\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[MODE_INGEST, MODE_RETRIEVE],\n            value=MODE_INGEST,\n            info=\"Switch between writing new data into the knowledge base and querying it.\",\n            real_time_refresh=True,\n            tool_mode=True,\n        ),\n        # --- Ingest-only inputs (default-shown; hidden when mode == Retrieve) -\n        HandleInput(\n            name=\"input_df\",\n            display_name=\"Input\",\n            info=(\n                \"Table with all original columns (already chunked / processed). \"\n                \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n                \"it is converted to a DataFrame automatically.\"\n            ),\n            input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n            required=True,\n            dynamic=True,\n            show=True,\n        ),\n        TableInput(\n            name=\"column_config\",\n            display_name=\"Column Configuration\",\n            info=\"Configure column behavior for the knowledge base.\",\n            required=True,\n            table_schema=[\n                {\n                    \"name\": \"column_name\",\n                    \"display_name\": \"Column Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Name of the column in the source DataFrame\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"vectorize\",\n                    \"display_name\": \"Vectorize\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Create embeddings for this column\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"identifier\",\n                    \"display_name\": \"Identifier\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Use this column as unique identifier\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"column_name\": \"text\",\n                    \"vectorize\": True,\n                    \"identifier\": True,\n                },\n            ],\n            dynamic=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"Batch size for processing embeddings\",\n            advanced=True,\n            value=1000,\n            dynamic=True,\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Embedding Provider API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        BoolInput(\n            name=\"allow_duplicates\",\n            display_name=\"Allow Duplicates\",\n            info=\"Allow duplicate rows in the knowledge base\",\n            advanced=True,\n            value=False,\n            dynamic=True,\n            show=True,\n        ),\n        StrInput(\n            name=\"metadata_json\",\n            display_name=\"Metadata\",\n            info=(\n                \"Optional JSON object of user metadata applied to every chunk produced by this \"\n                'run (e.g. {\"tag\": \"invoice\", \"year\": \"2026\"}). Same shape as the upload modal '\n                \"Metadata section so chunks browser filters + Knowledge retrieval metadata_filter \"\n                \"work uniformly across upload, folder, and flow-driven ingestion. Malformed JSON is \"\n                \"ignored with a warning rather than failing the run.\"\n            ),\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        # --- Retrieve-only inputs (default-hidden; shown when mode == Retrieve) -\n        MessageTextInput(\n            name=\"search_query\",\n            display_name=\"Search Query\",\n            info=\"Optional search query to filter knowledge base data.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of top results to return from the knowledge base.\",\n            value=5,\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_metadata\",\n            display_name=\"Include Metadata\",\n            info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n            value=True,\n            advanced=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_embeddings\",\n            display_name=\"Include Embeddings\",\n            info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n            value=False,\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"metadata_filter\",\n            display_name=\"Metadata Filter\",\n            info=(\n                \"Optional JSON object of user-metadata key/value pairs. Only chunks \"\n                'whose source_metadata matches every key are returned (e.g. {\"tag\": \"invoice\"} '\n                'or {\"tag\": [\"invoice\", \"audit\"]} for OR-of-values). Backends without '\n                \"native filtering apply the match client-side after retrieval.\"\n            ),\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n    ]\n\n    # ------ Outputs -------------------------------------------------------\n    # Both outputs are declared at the class level so the runtime can\n    # dispatch to either method depending on which one the saved flow has\n    # wired up. ``update_outputs`` filters the canvas-visible output per\n    # selected mode; see ``TypeConverterComponent`` and ``MemoryComponent``\n    # for the same pattern.\n    #\n    # Output names match the legacy ``KnowledgeIngestionComponent``\n    # (``dataframe_output``) and ``KnowledgeBaseComponent``\n    # (``retrieve_data``) so saved flow edges keyed on those names resolve\n    # cleanly against the merged component.\n    outputs = [\n        Output(\n            display_name=\"Results\",\n            name=\"dataframe_output\",\n            method=\"build_kb_info\",\n            types=[\"JSON\"],\n            selected=\"JSON\",\n        ),\n        Output(\n            display_name=\"Results\",\n            name=\"retrieve_data\",\n            method=\"retrieve_data\",\n            info=\"Returns the data from the selected knowledge base.\",\n            types=[\"Table\"],\n            selected=\"Table\",\n        ),\n    ]\n\n    # ------ Mode-driven UI updates ---------------------------------------\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"Sync the visible output with the current ``mode`` value on canvas load.\n\n        Saved flows hit this path: ``update_outputs`` is normally only triggered\n        by ``real_time_refresh`` field edits, so without this re-sync the\n        canvas could land with both outputs visible.\n        \"\"\"\n        await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = new_frontend_node.get(\"template\", {})\n        mode_value = template.get(\"mode\", {}).get(\"value\", MODE_INGEST)\n        self.update_outputs(new_frontend_node, \"mode\", mode_value)\n        # The rebuilt template carries class-default (Ingest) ``show`` flags,\n        # so a saved Retrieve node returned ``search_query`` hidden and the canvas\n        # silently dropped the edge feeding it.\n        self._apply_mode_visibility(template)\n        return new_frontend_node\n\n    def _apply_mode_visibility(self, build_config, field_name: str | None = None, field_value: Any = None):\n        \"\"\"Show only the inputs that belong to the effective mode.\"\"\"\n        current_mode = build_config.get(\"mode\", {}).get(\"value\") if isinstance(build_config, dict) else None\n        if field_name == \"mode\":\n            current_mode = field_value\n        # Map legacy/emoji-prefixed labels onto the current canonical values so\n        # flows saved before the label change still toggle visibility correctly.\n        if _is_retrieve_mode(current_mode):\n            current_mode = MODE_RETRIEVE\n        elif current_mode not in self.mode_config:\n            current_mode = MODE_INGEST\n        return set_current_fields(\n            build_config=build_config if isinstance(build_config, dotdict) else dotdict(build_config),\n            action_fields=self.mode_config,\n            selected_action=current_mode,\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Filter visible outputs to match the selected mode.\n\n        Triggered by the ``mode`` ``TabInput`` ``real_time_refresh`` flag.\n        The class-level ``outputs`` list always carries both entries so the\n        runtime can resolve either ``build_kb_info`` or ``retrieve_data``\n        regardless of canvas visibility — we just hide the unused one here.\n        \"\"\"\n        if field_name != \"mode\":\n            return frontend_node\n        if _is_retrieve_mode(field_value):\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"retrieve_data\",\n                    method=\"retrieve_data\",\n                    info=\"Returns the data from the selected knowledge base.\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                )\n            ]\n        else:\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"dataframe_output\",\n                    method=\"build_kb_info\",\n                    types=[\"JSON\"],\n                    selected=\"JSON\",\n                )\n            ]\n        return frontend_node\n\n    async def update_build_config(\n        self,\n        build_config,\n        field_value: Any,\n        field_name: str | None = None,\n    ):\n        \"\"\"Refresh KB options, drive the create-KB dialog, and hide off-mode fields.\"\"\"\n        # Astra-cloud gate covers both ingest and retrieve paths.\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        # Always populate the create-KB dialog's embedding-model options so the\n        # ModelInput renders correctly regardless of which input triggered the\n        # refresh.\n        try:\n            dialog_template = (\n                build_config[\"knowledge_base\"]\n                .get(\"dialog_inputs\", {})\n                .get(\"fields\", {})\n                .get(\"data\", {})\n                .get(\"node\", {})\n                .get(\"template\", {})\n            )\n            if \"02_embedding_model\" in dialog_template:\n                embedding_options = get_embedding_model_options(user_id=self.user_id)\n                dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n        except Exception:  # noqa: BLE001\n            self.log(\"Failed to populate embedding model options in dialog\")\n\n        # KB-picker refresh + create-new-KB flow (lifted from the legacy ingestion\n        # component verbatim; relied on by both the canvas refresh button and the\n        # dialog-submit path).\n        if field_name == \"knowledge_base\":\n            # Lazy import keeps lfx importable without langflow installed.\n            from langflow.services.database.models.user.crud import get_user_by_id\n\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for fetching knowledge base list.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n                if not current_user:\n                    msg = f\"User with ID {self.user_id} not found.\"\n                    raise ValueError(msg)\n            if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n                model_selection = field_value[\"02_embedding_model\"]\n                if isinstance(model_selection, dict):\n                    model_selection = [model_selection]\n\n                backend_type, backend_config = self._normalize_backend_selection(\n                    field_value.get(\"03_knowledge_backend\")\n                )\n                ensure_storage_routing_allowed(backend_config, is_superuser=bool(current_user.is_superuser))\n                new_kb_name = field_value[\"01_new_kb_name\"]\n                from langflow.api.utils.kb_helpers import local_chroma_rejection_reason, validate_kb_name\n\n                validate_kb_name(new_kb_name)\n                rejection = local_chroma_rejection_reason(backend_type, backend_config)\n                if rejection:\n                    raise ValueError(rejection)\n\n                embed_model = get_embeddings(\n                    model=model_selection,\n                    user_id=self.user_id,\n                )\n\n                try:\n                    await asyncio.wait_for(\n                        asyncio.to_thread(embed_model.embed_query, \"test\"),\n                        timeout=10,\n                    )\n                except TimeoutError as e:\n                    msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n                    raise ValueError(msg) from e\n                except Exception as e:\n                    msg = f\"Embedding validation failed: {e!s}\"\n                    raise ValueError(msg) from e\n\n                build_config[\"knowledge_base\"][\"value\"] = new_kb_name\n                await self._create_knowledge_base_record(\n                    user_id=self.user_id,\n                    name=field_value[\"01_new_kb_name\"],\n                    model_selection=model_selection,\n                    backend_type=backend_type,\n                    backend_config=backend_config,\n                )\n\n            build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(user_id=self.user_id)\n            if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n                build_config[\"knowledge_base\"][\"value\"] = None\n\n        # Honor the current mode regardless of which field triggered the refresh.\n        return self._apply_mode_visibility(build_config, field_name, field_value)\n\n    # =====================================================================\n    #                       INGESTION CODE PATH\n    # =====================================================================\n    @staticmethod\n    def _scalar_notna(value) -> bool:\n        \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n        ``pd.notna`` returns an array when given an array-like input, which\n        cannot be used directly in a boolean context.  This helper collapses\n        the result to a single scalar ``bool``.\n        \"\"\"\n        result = pd.notna(value)\n        if hasattr(result, \"__iter__\") and not isinstance(result, str):\n            import numpy as np\n\n            arr = np.asarray(result)\n            return arr.size > 0 and arr.all()\n        return bool(result)\n\n    def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n        \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n        if not self.column_config:\n            msg = \"Column configuration cannot be empty\"\n            raise ValueError(msg)\n\n        config_list = self.column_config if isinstance(self.column_config, list) else []\n\n        df_columns = set(df_source.columns)\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            if col_name not in df_columns:\n                msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n                raise ValueError(msg)\n\n        return config_list\n\n    @staticmethod\n    def _extract_source_types_from_df(df_source: pd.DataFrame) -> set[str]:\n        \"\"\"Pull file extensions out of common path/name columns on the source DataFrame.\n\n        The direct-upload ingestion path stores extensions in\n        the KB row's ``source_types`` so the KB list can render the\n        correct file-type icon. When ingestion happens via a connected\n        ``input_df`` (e.g. File → Knowledge) the same field stayed empty and\n        the icon defaulted to a blank tile. We look at the well-known columns\n        the File / S3 / cloud-storage components produce and collect any\n        plausible extension so the icon is consistent across both flows.\n        \"\"\"\n        candidate_columns = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for col in candidate_columns:\n            if col not in df_source.columns:\n                continue\n            for value in df_source[col].dropna():\n                ext = KnowledgeComponent._extension_from_value(value)\n                # Drop anything that doesn't look like an extension (URL\n                # query strings, version segments, etc.) — the icon palette\n                # keys off short alphanumeric tokens like \"pdf\"/\"docx\".\n                if ext:\n                    extensions.add(ext)\n        return extensions\n\n    @staticmethod\n    def _extension_from_value(value: Any) -> str | None:\n        \"\"\"Return a normalized file-extension token from a path / filename / MIME string.\n\n        Accepts values like ``\"report.PDF\"``, ``\"/docs/notes.txt\"``, or\n        ``\"application/pdf\"`` and returns ``\"pdf\"`` / ``\"txt\"``. Returns\n        ``None`` if no plausible extension can be derived.\n        \"\"\"\n        extension_length_limit = 10\n        if value is None:\n            return None\n        text = str(value).strip()\n        if not text:\n            return None\n        # MIME types like ``application/pdf`` carry the canonical extension\n        # in the subtype slot — preserve them so File / S3 messages keyed\n        # only on ``mimetype`` still resolve to an icon.\n        if \"/\" in text and \".\" not in text.rsplit(\"/\", 1)[-1]:\n            subtype = text.rsplit(\"/\", 1)[-1].strip().lower()\n            return subtype if subtype and len(subtype) <= extension_length_limit and subtype.isalnum() else None\n        if \".\" not in text:\n            return None\n        ext = text.rsplit(\".\", 1)[-1].strip().lower()\n        if ext and len(ext) <= extension_length_limit and ext.isalnum():\n            return ext\n        return None\n\n    @classmethod\n    def _extract_source_types_from_mapping(cls, mapping: Any) -> set[str]:\n        \"\"\"Pull extensions from a Message/Data-style ``data`` dict or a plain dict.\"\"\"\n        if not isinstance(mapping, dict):\n            return set()\n        candidate_keys = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for key in candidate_keys:\n            ext = cls._extension_from_value(mapping.get(key))\n            if ext:\n                extensions.add(ext)\n        return extensions\n\n    @classmethod\n    def _extract_source_types_from_input(cls, input_value: Any) -> set[str]:\n        \"\"\"Pull file extensions out of the raw component input.\n\n        ``convert_to_dataframe`` strips Message/Data fields down to ``text``\n        when projecting onto a DataFrame, so file metadata attached to a\n        File-component \"Raw Content\" output never reaches\n        ``_extract_source_types_from_df``. Looking at the raw input first\n        keeps the KB icon consistent with direct upload.\n        \"\"\"\n        if input_value is None:\n            return set()\n        if isinstance(input_value, list):\n            extensions: set[str] = set()\n            for item in input_value:\n                extensions |= cls._extract_source_types_from_input(item)\n            return extensions\n        if isinstance(input_value, pd.DataFrame):\n            return cls._extract_source_types_from_df(input_value)\n        # Message / Data / JSON all expose a ``data`` dict via the lfx schema.\n        mapping = getattr(input_value, \"data\", None)\n        if mapping is None and isinstance(input_value, dict):\n            mapping = input_value\n        return cls._extract_source_types_from_mapping(mapping)\n\n    async def _refresh_kb_stats(\n        self,\n        *,\n        kb_record_id: Any,\n        backend: BaseVectorStoreBackend,\n        extensions: set[str],\n    ) -> None:\n        \"\"\"Recount the KB from its vector store and write the totals onto the row.\n\n        Backend-agnostic: counts come from ``count`` / ``iter_documents`` /\n        ``storage_size_bytes``, so a Chroma, OpenSearch, or pgVector KB all report\n        real numbers. ``extensions`` are merged into ``source_types`` so the KB\n        list renders the right file-type icon regardless of which ingestion route\n        produced the chunks.\n\n        Best-effort: the chunks are already written and committed by the time this\n        runs, so a stats refresh failure must not fail the ingestion.\n        \"\"\"\n        if kb_record_id is None:\n            return\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return\n\n        try:\n            chunks = await backend.count()\n            characters = 0\n            words = 0\n            async with aclosing(backend.iter_documents()) as batches:\n                async for batch in batches:\n                    for document in batch:\n                        characters += len(document.content)\n                        words += len(document.content.split())\n\n            record = await knowledge_base_service.get_by_id(kb_record_id)\n            existing = set(record.source_types or []) if record is not None else set()\n\n            await knowledge_base_service.update_stats(\n                kb_record_id,\n                chunks=chunks,\n                words=words,\n                characters=characters,\n                size_bytes=await backend.storage_size_bytes(),\n                source_types=sorted(existing | extensions),\n            )\n        except Exception as e:  # noqa: BLE001 — stats are cosmetic; chunks are already written\n            self.log(f\"Warning: Could not refresh knowledge base stats: {e}\")\n\n    @staticmethod\n    def _default_backend_selection() -> tuple[str, dict[str, Any]]:\n        \"\"\"Deployment default for a new KB with no explicit backend chosen.\n\n        Environment-driven: pgVector when ``PGVECTOR_CONNECTION_STRING`` is set,\n        else OpenSearch when ``OPENSEARCH_URL`` is set, else SQLite. An\n        unspecified selection (headless flows, multi-replica) therefore lands on\n        the deployment's shared store.\n        \"\"\"\n        from lfx.base.knowledge_bases.backends.postgres import resolve_default_kb_backend\n\n        backend_type = resolve_default_kb_backend()\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, dict(_DEFAULT_OPENSEARCH_CONFIG)\n        return backend_type, {}\n\n    @classmethod\n    def _normalize_backend_selection(cls, value: Any) -> tuple[str, dict[str, Any]]:\n        \"\"\"Normalize a DBProviderInput value into backend type/config.\"\"\"\n        if not value:\n            return cls._default_backend_selection()\n\n        backend_config: dict[str, Any]\n        if isinstance(value, str):\n            backend_type, backend_config = value, {}\n        elif isinstance(value, dict):\n            backend_type = str(value.get(\"backend_type\") or value.get(\"id\") or BackendType.SQLITE.value)\n            backend_config = value.get(\"backend_config\") or value.get(\"config\") or {}\n        else:\n            msg = \"Invalid vector-store backend selection.\"\n            raise TypeError(msg)\n        if not isinstance(backend_config, dict):\n            msg = \"Vector-store backend configuration must be an object.\"\n            raise TypeError(msg)\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, {**_DEFAULT_OPENSEARCH_CONFIG, **backend_config}\n        if backend_type in {BackendType.SQLITE.value, BackendType.POSTGRES.value}:\n            return backend_type, backend_config\n        msg = \"Chroma is retired. Choose SQLite, pgVector, or OpenSearch for a new knowledge base.\"\n        raise ValueError(msg)\n\n    async def _create_knowledge_base_record(\n        self,\n        *,\n        user_id: Any,\n        name: str,\n        model_selection: list[dict[str, Any]],\n        backend_type: str,\n        backend_config: dict[str, Any],\n    ) -> None:\n        \"\"\"Persist the component-created KB in the DB when OpenXFlow is available.\"\"\"\n        from langflow.api.utils import knowledge_base_service\n\n        await knowledge_base_service.create_record(\n            user_id=user_id,\n            name=name,\n            model_selection=model_selection,\n            column_config=_normalize_column_config(self.column_config) if isinstance(self.column_config, list) else [],\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n        \"\"\"Build detailed column metadata.\"\"\"\n        metadata: dict[str, Any] = {\n            \"total_columns\": len(df_source.columns),\n            \"mapped_columns\": len(config_list),\n            \"unmapped_columns\": len(df_source.columns) - len(config_list),\n            \"columns\": [],\n            \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n        }\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            vectorize = coalesce_bool(config.get(\"vectorize\"))\n            identifier = coalesce_bool(config.get(\"identifier\"))\n\n            metadata[\"columns\"].append(\n                {\n                    \"name\": col_name,\n                    \"vectorize\": vectorize,\n                    \"identifier\": identifier,\n                }\n            )\n\n            if vectorize:\n                metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n            if identifier:\n                metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n        return metadata\n\n    async def _create_vector_store(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        \"\"\"Create vector store using the configured DB provider.\"\"\"\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            return await self._ingest_into_backend(backend, df_source, config_list, embedding_function, job_id=job_id)\n        except BaseException:\n            await _finish_despite_cancellation(backend.teardown())\n            raise\n\n    async def _ingest_into_backend(\n        self,\n        backend: BaseVectorStoreBackend,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        await backend.ensure_ready()\n\n        from langflow.services.knowledge_base_storage.runtime import operation, resolve_record\n\n        record = await resolve_record(self._user_uuid, self.knowledge_base)\n        async with operation(record, shared=True):\n            existing_ids = set()\n            if not self.allow_duplicates:\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        for document in batch:\n                            doc_id = document.metadata.get(\"_id\")\n                            if doc_id:\n                                existing_ids.add(doc_id)\n\n        data_objects = await self._convert_df_to_data_objects(df_source, config_list, existing_ids=existing_ids)\n\n        user_metadata_tag = self._resolve_user_metadata_tag()\n\n        documents = []\n        for data_obj in data_objects:\n            doc = data_obj.to_lc_document()\n            if user_metadata_tag:\n                doc.metadata[\"source_metadata\"] = user_metadata_tag\n            documents.append(doc)\n        if not documents:\n            return backend\n\n        # No storage lease may surround the provider call. The write lease\n        # below rechecks routing and IDs after concurrent operations finish.\n        vectors = await self._embed_unless_job_cancelled(\n            embedding_function, [doc.page_content for doc in documents], job_id\n        )\n        if len(vectors) != len(documents):\n            msg = \"Embedding provider returned an incorrect number of vectors\"\n            raise BackendConfigurationError(msg)\n        embedded = [\n            IngestedDocument(doc.page_content, doc.metadata, vector, id=doc.id)\n            for doc, vector in zip(documents, vectors, strict=True)\n        ]\n        async with operation(record):\n            # A cancel that landed after the embeddings returned must still\n            # keep this run's chunks out of the KB.\n            await self._raise_if_job_cancelled(job_id)\n            if not self.allow_duplicates:\n                current_ids = set()\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        current_ids.update(doc.metadata[\"_id\"] for doc in batch if doc.metadata.get(\"_id\"))\n                unique = []\n                for doc in embedded:\n                    doc_id = doc.metadata.get(\"_id\")\n                    if doc_id and doc_id in current_ids:\n                        continue\n                    if doc_id:\n                        current_ids.add(doc_id)\n                    unique.append(doc)\n                embedded = unique\n\n            if embedded:\n                await backend.add_embedded_documents(embedded)\n                self.log(f\"Added {len(embedded)} documents to vector store '{self.knowledge_base}'\")\n\n        return backend\n\n    async def _embed_unless_job_cancelled(\n        self, embedding_function, texts: list[str], job_id: uuid.UUID | None\n    ) -> list[list[float]]:\n        \"\"\"Embed ``texts``, giving up as soon as the run's job is cancelled.\n\n        The Knowledge page cancels a run by marking its job. The embedding call\n        is the long, write-free part of a flow ingestion, so it is the safe\n        place to stop.\n        \"\"\"\n        await self._raise_if_job_cancelled(job_id)\n        if job_id is None:\n            return await embedding_function.aembed_documents(texts)\n\n        # A task group would wrap provider errors in an ExceptionGroup, so a\n        # plain watcher task cancels this scope instead.\n        with anyio.CancelScope() as scope:\n            watcher = asyncio.ensure_future(self._cancel_when_job_cancelled(job_id, scope))\n            try:\n                return await embedding_function.aembed_documents(texts)\n            finally:\n                # The watcher may be mid-poll; let its DB session close.\n                watcher.cancel()\n                with anyio.CancelScope(shield=True):\n                    await asyncio.wait({watcher})\n        raise self._cancelled_error()\n\n    async def _cancel_when_job_cancelled(self, job_id: uuid.UUID, scope: anyio.CancelScope) -> None:\n        # The cancel is a job-row write from another request, so it is polled.\n        while True:\n            await asyncio.sleep(JOB_CANCEL_POLL_SECONDS)\n            if await self._job_cancelled(job_id):\n                scope.cancel()\n                return\n\n    async def _raise_if_job_cancelled(self, job_id: uuid.UUID | None) -> None:\n        if await self._job_cancelled(job_id):\n            raise self._cancelled_error()\n\n    def _cancelled_error(self) -> KnowledgeIngestionCancelledError:\n        return KnowledgeIngestionCancelledError(f\"Ingestion into '{self.knowledge_base}' was cancelled.\")\n\n    async def _job_cancelled(self, job_id: uuid.UUID | None) -> bool:\n        if job_id is None:\n            return False\n        try:\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError:\n            return False\n        try:\n            job = await get_job_service().get_job_by_job_id(job_id)\n        except Exception as exc:  # noqa: BLE001 — a failed poll must not abort the ingestion\n            self.log(f\"Could not check whether ingestion job {job_id} was cancelled: {exc}\")\n            return False\n        return job is not None and job.status == JobStatus.CANCELLED\n\n    async def _convert_df_to_data_objects(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        existing_ids: set[str] | None = None,\n    ) -> list[Data]:\n        \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n        data_objects: list[Data] = []\n\n        if existing_ids is None:\n            existing_ids = set()\n\n        content_cols = []\n        identifier_cols = []\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n\n            if coalesce_bool(config.get(\"vectorize\")):\n                content_cols.append(col_name)\n            if coalesce_bool(config.get(\"identifier\")):\n                identifier_cols.append(col_name)\n\n        for _, row in df_source.iterrows():\n            identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n            page_content = \" \".join(identifier_parts)\n\n            data_dict = {\n                \"text\": page_content,\n            }\n\n            if identifier_cols:\n                identifier_parts = [\n                    str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n                ]\n                page_content = \" \".join(identifier_parts)\n\n            for col in df_source.columns:\n                if col not in content_cols and col in row and self._scalar_notna(row[col]):\n                    value = row[col]\n                    data_dict[col] = str(value)\n\n            page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n            data_dict[\"_id\"] = page_content_hash\n\n            if not self.allow_duplicates and page_content_hash in existing_ids:\n                self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n                continue\n\n            data_obj = Data(data=data_dict)\n            data_objects.append(data_obj)\n\n        return data_objects\n\n    def is_valid_collection_name(self, name: str) -> bool:\n        \"\"\"Return whether ``name`` satisfies the shared collection-name contract.\"\"\"\n        return is_valid_kb_collection_name(name)\n\n    def _resolve_store_path(\n        self,\n        kb_user: str,\n        backend_type: str,\n        backend_config: dict[str, Any] | None,\n    ) -> Path | None:\n        \"\"\"Return this KB's on-disk directory, or ``None`` when it needs no disk.\n\n        Only local Chroma stores anything on this box; every other backend\n        resolves to ``None`` and never touches the filesystem.\n        \"\"\"\n        # Lazy import keeps lfx importable without langflow's DB services.\n        from langflow.api.utils.kb_helpers import resolve_local_store_path\n\n        return resolve_local_store_path(\n            self.knowledge_base,\n            kb_user,\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    async def _kb_username(self) -> str:\n        \"\"\"Resolve the owning user's username (used to root a local-Chroma path).\"\"\"\n        cached = getattr(self, \"_cached_kb_username\", None)\n        if cached is not None:\n            return cached\n\n        # Lazy import to keep ``lfx`` importable standalone — langflow's\n        # user/DB models are not always available at module load time.\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        async with session_scope() as db:\n            if not self.user_id:\n                msg = \"User ID is required for fetching knowledge base path.\"\n                raise ValueError(msg)\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n            self._cached_kb_username = current_user.username\n\n        return self._cached_kb_username\n\n    async def _kb_path(self) -> Path | None:\n        \"\"\"Resolve the KB's local directory from its configured backend.\"\"\"\n        cached_path = getattr(self, \"_cached_kb_path\", None)\n        if cached_path is not None:\n            return cached_path\n\n        backend_type, backend_config = await self._resolve_backend_config()\n        kb_user = await self._kb_username()\n        self._cached_kb_path = self._resolve_store_path(kb_user, backend_type, backend_config)\n        return self._cached_kb_path\n\n    def _resolve_user_metadata_tag(self) -> str:\n        \"\"\"Return the JSON-encoded user metadata tag for chunk writes.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return \"\"\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return \"\"\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError) as exc:\n            self.log(f\"KnowledgeComponent: metadata_json is not valid JSON ({exc}); skipping metadata stamp.\")\n            return \"\"\n        if not isinstance(decoded, dict):\n            self.log(\"KnowledgeComponent: metadata_json must decode to a JSON object; skipping metadata stamp.\")\n            return \"\"\n        return json.dumps(decoded, sort_keys=True)\n\n    async def build_kb_info(self) -> Data:\n        \"\"\"Main ingestion routine → returns a dict with KB metadata.\n\n        The annotation is intentionally narrowed to ``Data`` even though the\n        cross-mode fallback below may return a ``DataFrame`` from\n        ``retrieve_data``. The frontend builds React-Flow handle IDs from\n        this output's type list; widening it to ``Data | DataFrame`` makes\n        the API advertise ``[\"JSON\", \"Table\"]`` for the ingest output and\n        breaks every saved-edge sourceHandle that was generated against\n        a single-type handle (BUG-02). Python doesn't enforce return\n        annotations at runtime, so the rare fallback path keeps working.\n        \"\"\"\n        if _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.retrieve_data()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        run_id: uuid.UUID | None = None\n        run_job_id: uuid.UUID | None = None\n        run_summary: IngestionSummary | None = None\n        run_status: IngestionRunStatus = IngestionRunStatus.SUCCEEDED\n        run_error: str | None = None\n        kb_record_id: uuid.UUID | None = None\n        previous_kb_status = \"ready\"\n        previous_failure_reason: str | None = None\n        try:\n            input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n            df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n            config_list = self._validate_column_config(df_source)\n            column_metadata = self._build_column_metadata(config_list, df_source)\n\n            # Embedding config comes from the ``knowledge_base`` row — the sole\n            # authority. No sidecar is read, so ingestion resolves the same model\n            # on any replica.\n            stored_metadata = await self._get_kb_metadata()\n            # A cancelled run restores this status. Only an earlier failure is\n            # kept; \"empty\" is an API projection of a ready KB with no chunks.\n            if stored_metadata.get(\"status\") == \"failed\":\n                previous_kb_status = \"failed\"\n                previous_failure_reason = stored_metadata.get(\"failure_reason\")\n            model_selection = stored_metadata.get(\"model_selection\")\n            if model_selection:\n                model_selection = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            else:\n                embedding_model_name = stored_metadata.get(\"embedding_model\")\n                embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n                if embedding_model_name:\n                    try:\n                        all_options = get_embedding_model_options(user_id=self.user_id)\n                        match = next(\n                            (o for o in all_options if o.get(\"name\") == embedding_model_name),\n                            None,\n                        )\n                        if match:\n                            model_selection = [match]\n                        else:\n                            self.log(\n                                f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n                                \"from the knowledge base record is no longer available in the model registry. \"\n                                \"Please re-create this knowledge base with a supported embedding model.\"\n                            )\n                            msg = (\n                                f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n                                \"The knowledge base was created with an older format and the model \"\n                                \"is not available in the current registry. \"\n                                \"Please re-create the knowledge base with a supported embedding model.\"\n                            )\n                            raise ValueError(msg)\n                    except ValueError:\n                        raise\n                    except Exception:  # noqa: BLE001\n                        self.log(\n                            f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n                            \"Please re-create this knowledge base with a supported embedding model.\"\n                        )\n                        msg = (\n                            f\"Could not look up embedding model '{embedding_model_name}' \"\n                            f\"(provider: {embedding_provider}). \"\n                            \"Please re-create the knowledge base with a supported embedding model.\"\n                        )\n                        raise ValueError(msg)  # noqa: B904\n\n            if not model_selection:\n                msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n                raise ValueError(msg)\n\n            # ``api_key=None`` falls through to the user's variables table and\n            # then the environment inside ``get_embeddings`` — the same\n            # resolution the server-side ingestion path uses. The component input\n            # only overrides it.\n            embedding_function = get_embeddings(\n                model=model_selection,\n                user_id=self.user_id,\n                api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n                chunk_size=self.chunk_size,\n            )\n\n            kb_path = await self._kb_path()\n\n            run_id, run_job_id, run_summary, kb_record_id = await self._begin_ingestion_run(kb_path)\n            if kb_record_id is not None:\n                await self._record_kb_status(kb_record_id, \"ingesting\")\n\n            backend = await self._create_vector_store(\n                df_source, config_list, embedding_function=embedding_function, job_id=run_job_id\n            )\n\n            try:\n                # Stamp the KB with the file extensions we just ingested so\n                # the Knowledge Bases list renders the correct icon for\n                # flow-driven ingestion (input_df), matching direct upload.\n                # We look at the raw input first because ``convert_to_dataframe``\n                # drops Message/Data metadata fields (file_path, mimetype, …)\n                # when projecting onto the DataFrame.\n                source_types = self._extract_source_types_from_input(input_value)\n                source_types |= self._extract_source_types_from_df(df_source)\n                # Storage routing returns a guarded proxy with the backend's\n                # methods, so a concrete-class check would skip the refresh.\n                await self._refresh_kb_stats(\n                    kb_record_id=kb_record_id,\n                    backend=backend,\n                    extensions=source_types,\n                )\n            finally:\n                await _finish_despite_cancellation(backend.teardown())\n\n            meta: dict[str, Any] = {\n                \"kb_id\": str(uuid.uuid4()),\n                \"kb_name\": self.knowledge_base,\n                \"rows\": len(df_source),\n                \"column_metadata\": column_metadata,\n                \"path\": str(kb_path),\n                \"config_columns\": len(config_list),\n                \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n            }\n\n            if run_summary is not None:\n                run_summary.record_item(\n                    IngestionItemResult(\n                        item_id=self.knowledge_base,\n                        display_name=f\"{self.knowledge_base} ({len(df_source)} rows)\",\n                        status=IngestionItemStatus.SUCCEEDED,\n                        chunks_created=len(df_source),\n                    ),\n                )\n\n            if kb_record_id is not None:\n                # Stats were already refreshed straight from the backend above.\n                await self._record_kb_status(kb_record_id, \"ready\")\n\n            self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n            return Data(data=meta)\n\n        except asyncio.CancelledError:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = \"Flow ingestion was cancelled.\"\n            raise\n        except KnowledgeIngestionCancelledError as e:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = str(e)\n            raise\n        except (OSError, ValueError, RuntimeError, KeyError) as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            msg = f\"Error during KB ingestion: {e}\"\n            raise RuntimeError(msg) from e\n        except Exception as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            raise\n        finally:\n\n            async def settle_run() -> None:\n                if run_id is not None and run_summary is not None:\n                    await self._finalize_ingestion_run(\n                        run_id=run_id,\n                        job_id=run_job_id,\n                        summary=run_summary,\n                        status=run_status,\n                        error_message=run_error,\n                    )\n                if kb_record_id is not None:\n                    if run_status is IngestionRunStatus.FAILED:\n                        await self._record_kb_status(kb_record_id, \"failed\", failure_reason=run_error)\n                    elif run_status is IngestionRunStatus.CANCELLED:\n                        await self._record_kb_status(\n                            kb_record_id, previous_kb_status, failure_reason=previous_failure_reason\n                        )\n\n            # A stopped build can cancel this task more than once, and the\n            # writes that make the run terminal must survive every cancel.\n            await _finish_despite_cancellation(settle_run())\n\n    async def _begin_ingestion_run(\n        self,\n        kb_path: Path,\n    ) -> tuple[uuid.UUID | None, uuid.UUID | None, IngestionSummary | None, uuid.UUID | None]:\n        \"\"\"Create a parent ``Job`` and seed an ingestion-run row.\"\"\"\n        if not self.user_id:\n            self.log(\"No user_id on component; skipping ingestion-run tracking.\")\n            return None, None, None, None\n\n        try:\n            user_uuid = uuid.UUID(str(self.user_id))\n        except (ValueError, TypeError) as exc:\n            self.log(f\"Could not coerce user_id={self.user_id!r} to UUID; skipping run tracking ({exc}).\")\n            return None, None, None, None\n\n        try:\n            from langflow.api.utils import ingestion_run_service, knowledge_base_service\n            from langflow.services.database.models.jobs.model import JobStatus, JobType\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion will not be recorded ({exc}).\")\n            return None, None, None, None\n\n        job_id: uuid.UUID | None = None\n        run_id: uuid.UUID | None = None\n        summary: IngestionSummary | None = None\n        try:\n            kb_record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n            kb_record_id = kb_record.id if kb_record is not None else None\n\n            user_metadata = self._parse_user_metadata_dict()\n\n            job_id = uuid.uuid4()\n            job_service = get_job_service()\n            raw_flow_id = getattr(self, \"flow_id\", None)\n            flow_id_uuid = uuid.UUID(str(raw_flow_id)) if raw_flow_id else job_id\n            source = FlowComponentSource(\n                user_id=user_uuid,\n                source_config={\n                    \"knowledge_base\": self.knowledge_base,\n                    \"kb_path\": str(kb_path),\n                    \"flow_id\": str(flow_id_uuid),\n                },\n            )\n            summary = IngestionSummary(\n                kb_name=self.knowledge_base,\n                source_type=source.source_type.value,\n                user_id=user_uuid,\n                job_id=job_id,\n                source_config=source.describe().get(\"config\") or {},\n                user_metadata=user_metadata,\n            )\n            # Inserted running and already heartbeated, so an orphan sweep in\n            # another worker never sees this live run without a heartbeat.\n            await job_service.create_job(\n                job_id=job_id,\n                flow_id=flow_id_uuid,\n                job_type=JobType.INGESTION,\n                asset_id=kb_record_id,\n                asset_type=\"knowledge_base\",\n                user_id=user_uuid,\n                status=JobStatus.IN_PROGRESS,\n                heartbeat=True,\n            )\n\n            run_id = await ingestion_run_service.create_run(\n                kb_name=self.knowledge_base,\n                source=source,\n                job_id=job_id,\n                user_id=user_uuid,\n                kb_id=kb_record_id,\n                user_metadata=user_metadata,\n            )\n            if run_id is None:\n                return None, job_id, None, kb_record_id\n            await ingestion_run_service.mark_running(run_id)\n\n            self.log(f\"Started ingestion run job_id={job_id} kb_name={self.knowledge_base} kb_id={kb_record_id}\")\n            # Keeps the job heartbeated until _finalize_ingestion_run stops it.\n            await job_service.start_keep_alive(job_id)\n        except asyncio.CancelledError:\n            # The caller has not received the run ID yet, so it cannot finalize\n            # a cancellation that arrives while tracking is being initialized.\n            if job_id is not None and summary is not None:\n                await _finish_despite_cancellation(\n                    self._finalize_ingestion_run(\n                        run_id=run_id or job_id,\n                        job_id=job_id,\n                        summary=summary,\n                        status=IngestionRunStatus.CANCELLED,\n                        error_message=\"Flow ingestion was cancelled.\",\n                    )\n                )\n            raise\n        except Exception as exc:  # noqa: BLE001 — telemetry must never abort ingestion\n            self.log(f\"Could not begin ingestion-run tracking: {exc}\")\n            return None, None, None, None\n        else:\n            return run_id, job_id, summary, kb_record_id\n\n    async def _finalize_ingestion_run(\n        self,\n        *,\n        run_id: uuid.UUID,\n        job_id: uuid.UUID | None,\n        summary: IngestionSummary,\n        status: IngestionRunStatus,\n        error_message: str | None,\n    ) -> None:\n        \"\"\"Persist the final summary and transition the parent Job.\"\"\"\n        try:\n            from langflow.api.utils import ingestion_run_service\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion-run finalize skipped ({exc}).\")\n            return\n\n        try:\n            if job_id is not None:\n                # The heartbeat also rewrites job_metadata, so it stops first.\n                await get_job_service().stop_keep_alive(job_id)\n            await ingestion_run_service.finalize_run(\n                run_id,\n                summary=summary,\n                status=status,\n                error_message=error_message,\n            )\n            if job_id is not None:\n                terminal_status = {\n                    IngestionRunStatus.FAILED: JobStatus.FAILED,\n                    IngestionRunStatus.CANCELLED: JobStatus.CANCELLED,\n                }.get(status, JobStatus.COMPLETED)\n                await get_job_service().update_job_status(job_id, terminal_status, finished_timestamp=True)\n        except Exception as exc:  # noqa: BLE001 — telemetry must never re-raise\n            self.log(f\"Could not finalize ingestion-run tracking: {exc}\")\n\n    async def _record_kb_status(\n        self,\n        kb_record_id: uuid.UUID,\n        status_value: str,\n        *,\n        failure_reason: str | None = None,\n    ) -> None:\n        \"\"\"Mirror Path A's KB-row status transitions.\"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n            from langflow.services.database.models.knowledge_base.model import KnowledgeBaseStatus\n        except ImportError:\n            return\n        try:\n            await knowledge_base_service.update_status(\n                kb_record_id,\n                status=KnowledgeBaseStatus(status_value),\n                failure_reason=failure_reason,\n            )\n        except Exception as exc:  # noqa: BLE001\n            self.log(f\"Could not update KB status to {status_value}: {exc}\")\n\n    def _parse_user_metadata_dict(self) -> dict[str, Any]:\n        \"\"\"Decode ``metadata_json`` to a dict, or ``{}`` on any error.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return {}\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return {}\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError):\n            return {}\n        return decoded if isinstance(decoded, dict) else {}\n\n    # =====================================================================\n    #                       RETRIEVAL CODE PATH\n    # =====================================================================\n    @property\n    def _user_uuid(self) -> uuid.UUID | None:\n        \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n        if not self.user_id:\n            return None\n        return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n    async def _get_kb_metadata(self, knowledge_base: str | None = None) -> dict:\n        \"\"\"Load this knowledge base's embedding config from its database row.\n\n        The row is the sole authority. There is no on-disk sidecar to fall back\n        to, which is what lets retrieval work identically on a replica whose\n        filesystem never held the KB directory.\n        \"\"\"\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return {}\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return {}\n        record = await knowledge_base_service.get_by_user_and_name(\n            user_uuid,\n            knowledge_base or self.knowledge_base,\n        )\n        if record is None:\n            return {}\n        return knowledge_base_service.record_to_metadata_dict(record)\n\n    async def _backend_from_record(self) -> tuple[str, dict[str, Any]] | None:\n        \"\"\"Read ``(backend_type, backend_config)`` off the KB's database row.\n\n        Returns ``None`` when there is no row to read — a legacy KB predating the\n        record, or bare lfx with no langflow installed. A lookup that *fails* is a\n        different situation and is allowed to propagate: guessing a backend after a\n        database error is how vectors end up in a store nothing queries.\n        \"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return None\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return None\n        record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n        if record is None:\n            return None\n        return record.backend_type or BackendType.CHROMA.value, record.backend_config or {}\n\n    async def _resolve_backend_config(self) -> tuple[str, dict[str, Any]]:\n        \"\"\"Resolve this KB's backend from its database row.\n\n        Ingestion and retrieval both land here, so they cannot disagree about\n        where this KB's vectors live. A backend that cannot be resolved raises\n        rather than defaulting to local storage: guessing is how a pod ends up\n        writing vectors to a local directory while queries follow the real config\n        to a remote store and silently find nothing.\n        \"\"\"\n        resolved = await self._backend_from_record()\n        if resolved is None:\n            msg = (\n                f\"Cannot determine the vector-store backend for knowledge base \"\n                f\"'{self.knowledge_base}': it has no database record. Refusing to fall \"\n                f\"back to local storage, which would write to a different store than \"\n                f\"queries read from. If this knowledge base predates database-backed \"\n                f\"metadata, adopt it with 'langflow reconcile-kb-from-disk'.\"\n            )\n            raise ValueError(msg)\n        return resolved\n\n    def _resolve_model_selection(self, metadata: dict[str, Any]) -> list[dict[str, Any]]:\n        \"\"\"Resolve the ``get_embeddings``-compatible model selection from metadata.\"\"\"\n        model_selection = metadata.get(\"model_selection\")\n        if model_selection:\n            selection_list = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            return [self._hydrate_model_metadata(entry) for entry in selection_list]\n\n        embedding_model_name = metadata.get(\"embedding_model\")\n        embedding_provider = metadata.get(\"embedding_provider\", \"Unknown\")\n        if not embedding_model_name:\n            msg = (\n                f\"Knowledge base '{self.knowledge_base}' has no embedding model recorded; \"\n                \"re-create it with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n\n        match = self._find_catalog_entry(embedding_model_name)\n        if match is None:\n            msg = (\n                f\"Embedding model '{embedding_model_name}' (provider '{embedding_provider}') \"\n                \"recorded for this knowledge base is no longer available in the model registry. \"\n                \"Please re-create the knowledge base with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n        return [match]\n\n    def _hydrate_model_metadata(self, entry: dict[str, Any]) -> dict[str, Any]:\n        \"\"\"Fill in ``metadata.embedding_class`` / ``param_mapping`` if missing.\"\"\"\n        entry_metadata = entry.get(\"metadata\") or {}\n        has_class = bool(entry_metadata.get(\"embedding_class\"))\n        has_mapping = bool(entry_metadata.get(\"param_mapping\"))\n        if has_class and has_mapping:\n            return entry\n\n        model_name = entry.get(\"name\")\n        if not model_name:\n            return entry\n\n        catalog_entry = self._find_catalog_entry(model_name)\n        if catalog_entry is None:\n            return entry\n\n        catalog_metadata = catalog_entry.get(\"metadata\") or {}\n        merged_metadata = {**catalog_metadata, **entry_metadata}\n        return {**entry, \"metadata\": merged_metadata}\n\n    def _find_catalog_entry(self, model_name: str) -> dict[str, Any] | None:\n        \"\"\"Look up an embedding model by name in the unified-models catalog.\"\"\"\n        options = get_embedding_model_options(user_id=self.user_id)\n        return next((o for o in options if o.get(\"name\") == model_name), None)\n\n    async def retrieve_data(self) -> DataFrame:\n        \"\"\"Retrieve data from the selected knowledge base.\n\n        Annotation narrowed to ``DataFrame`` to keep this output's handle\n        type list at ``[\"Table\"]`` only; widening to a union surfaces\n        ``[\"Table\", \"JSON\"]`` on the API and breaks every starter-project\n        edge whose sourceHandle was stored against a single-type handle\n        (BUG-02). The cross-mode defensive fallback may still return a\n        ``Data`` at runtime — Python ignores return annotations, so\n        nothing breaks.\n        \"\"\"\n        if not _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.build_kb_info()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        if not self.user_id:\n            msg = \"User ID is required for fetching Knowledge Base data.\"\n            raise ValueError(msg)\n\n        # Saved flows can load in standalone lfx, but knowledge retrieval\n        # requires the application's user database and storage services.\n        try:\n            from langflow.services.database.models.user.crud import get_user_by_id\n        except ModuleNotFoundError as exc:\n            if exc.name != \"langflow\":\n                raise\n            msg = \"Knowledge Base retrieval requires OpenXFlow's application services. Run this flow in OpenXFlow.\"\n            raise RuntimeError(msg) from None\n\n        async with session_scope() as db:\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n\n        metadata = await self._get_kb_metadata()\n        if not metadata:\n            msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n            raise ValueError(msg)\n\n        model_selection = self._resolve_model_selection(metadata)\n        chunk_size = metadata.get(\"chunk_size\")\n\n        # Resolve where this KB lives before instantiating embeddings: path\n        # containment is a cheap local check, and a request that will be refused\n        # shouldn't first pay for a credential lookup and a provider client.\n        backend_type, _backend_config = await self._resolve_backend_config()\n\n        # ``api_key=None`` is not \"no credential\": ``get_embeddings`` resolves the\n        # provider's key from the user's variables table and then the environment,\n        # exactly as the server-side ingestion path does. The component input just\n        # takes precedence when supplied.\n        embedding_function = get_embeddings(\n            model=model_selection,\n            user_id=self.user_id,\n            api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n            chunk_size=chunk_size,\n        )\n\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            user_metadata_filter = _parse_metadata_filter(getattr(self, \"metadata_filter\", None))\n            use_scores = bool(self.search_query)\n            exact_filter = backend_type == BackendType.SQLITE.value\n            search_k = self.top_k if exact_filter or not user_metadata_filter else self.top_k * 4\n            search_options = {\"source_filter\": user_metadata_filter} if exact_filter else {}\n            results = await backend.similarity_search(\n                query=self.search_query or \"\",\n                k=search_k,\n                with_scores=use_scores,\n                **search_options,\n            )\n            if user_metadata_filter and not exact_filter:\n                results = [\n                    (doc, score) for doc, score in results if _chunk_matches_filter(doc.metadata, user_metadata_filter)\n                ][: self.top_k]\n\n            embeddings_by_key: dict[tuple[str, str], list[float]] = {}\n            if self.include_embeddings and results:\n                # Join each retrieved chunk to its stored embedding. We key on\n                # ``_id`` when present and fall back to page content otherwise,\n                # so KBs populated by direct file upload — whose chunks carry no\n                # ``_id`` — still resolve. See ``_embedding_match_key``.\n                wanted_keys = {_embedding_match_key(doc.page_content, doc.metadata) for doc, _score in results}\n                async with aclosing(backend.iter_documents(include_embeddings=True)) as batches:\n                    async for batch in batches:\n                        for entry in batch:\n                            if entry.embedding is None:\n                                continue\n                            key = _embedding_match_key(entry.content, entry.metadata)\n                            if key in wanted_keys:\n                                embeddings_by_key[key] = entry.embedding\n                        if wanted_keys <= embeddings_by_key.keys():\n                            break\n\n            data_list: list[Data] = []\n            for doc, score in results:\n                kwargs: dict[str, Any] = {\"content\": doc.page_content}\n                if use_scores:\n                    kwargs[\"_score\"] = backend.normalize_score(score)\n                if self.include_metadata:\n                    kwargs.update(doc.metadata)\n                if self.include_embeddings:\n                    kwargs[\"_embeddings\"] = embeddings_by_key.get(_embedding_match_key(doc.page_content, doc.metadata))\n                data_list.append(Data(**kwargs))\n\n            return DataFrame(data=data_list)\n        finally:\n            await backend.teardown()\n\n\ndef _embedding_match_key(content: str, metadata: dict[str, Any] | None) -> tuple[str, str]:\n    \"\"\"Build a stable key for aligning a retrieved chunk with its stored embedding.\n\n    Embeddings are gathered separately (via ``iter_documents``) and then joined\n    back onto the search results. Component-driven ingestion stamps a\n    content-hash ``_id`` on every chunk, but direct file-upload ingestion\n    (``KBIngestionHelper.perform_ingestion``) does not. Keying the join purely on\n    ``_id`` therefore left every upload-populated KB with ``_embeddings: None``.\n\n    We prefer ``_id`` when present (so legitimately distinct chunks that happen to\n    share text aren't collapsed) and fall back to the chunk's page content\n    otherwise. The content fallback is exact for the embedding use case: two\n    chunks with identical text necessarily share the same embedding vector. The\n    leading ``\"id\"`` / ``\"content\"`` tag namespaces the two key spaces so a mixed\n    KB (some chunks with ``_id``, some without) never cross-matches.\n    \"\"\"\n    doc_id = metadata.get(\"_id\") if metadata else None\n    if doc_id:\n        return (\"id\", str(doc_id))\n    return (\"content\", content or \"\")\n\n\ndef _parse_metadata_filter(raw: str | None) -> dict[str, list[str]]:\n    \"\"\"Decode the ``metadata_filter`` input into a {key: [values]} map.\n\n    Empty or malformed input maps to an empty filter so retrieval falls back\n    to the unfiltered path. JSON errors are swallowed here rather than raised:\n    surfacing component-config errors at the canvas node would break a flow\n    run for what is meant to be an optional refinement.\n    \"\"\"\n    if not raw:\n        return {}\n    text = raw.strip() if isinstance(raw, str) else raw\n    if not text:\n        return {}\n    try:\n        decoded = json.loads(text)\n    except (TypeError, json.JSONDecodeError):\n        logger.warning(\"KnowledgeComponent: metadata_filter is not valid JSON; ignoring filter.\")\n        return {}\n    if not isinstance(decoded, dict):\n        logger.warning(\"KnowledgeComponent: metadata_filter must be a JSON object; ignoring filter.\")\n        return {}\n    result: dict[str, list[str]] = {}\n    for key, value in decoded.items():\n        if not isinstance(key, str):\n            continue\n        if isinstance(value, list):\n            result[key] = [str(entry) for entry in value]\n        else:\n            result[key] = [str(value)]\n    return result\n\n\ndef _chunk_matches_filter(metadata: dict[str, Any] | None, filt: dict[str, list[str]]) -> bool:\n    \"\"\"AND across keys, OR within key values, mirroring the chunks endpoint.\"\"\"\n    if not filt:\n        return True\n    if not metadata:\n        return False\n    raw = metadata.get(\"source_metadata\")\n    if not raw:\n        return False\n    try:\n        stored = json.loads(raw) if isinstance(raw, str) else raw\n    except json.JSONDecodeError:\n        return False\n    if not isinstance(stored, dict):\n        return False\n    for key, expected_values in filt.items():\n        actual = stored.get(key)\n        if actual is None:\n            return False\n        actual_set = {str(entry) for entry in actual} if isinstance(actual, list) else {str(actual)}\n        if not actual_set & set(expected_values):\n            return False\n    return True\n"
+                "value": "\"\"\"Unified Knowledge component — ingest into or retrieve from a knowledge base.\n\nThis component merges what used to live in ``ingestion.py`` and\n``retrieval.py`` into a single, mode-driven component. A ``TabInput``\n(\"📥 Ingest\" / \"🔍 Retrieve\") drives which inputs and which output are\nvisible. The merged shape gives users one node per KB instead of two\nparallel ones that always shared the same ``knowledge_base`` picker,\nembedding-model metadata, and backend registry.\n\nBoth legacy classes (``KnowledgeIngestionComponent``,\n``KnowledgeBaseComponent``) remain importable as thin subclasses of\n``KnowledgeComponent`` — see the sibling ``ingestion.py`` / ``retrieval.py``\nmodules — so saved flows continue to load unchanged.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport uuid\nfrom collections.abc import Awaitable, Mapping\nfrom contextlib import aclosing\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, TypeVar\n\nimport anyio\nimport pandas as pd\n\nfrom lfx.base.knowledge_bases.backends import BackendType, BaseVectorStoreBackend\nfrom lfx.base.knowledge_bases.backends.base import BackendConfigurationError, IngestedDocument\nfrom lfx.base.knowledge_bases.backends.naming import ensure_storage_routing_allowed\nfrom lfx.base.knowledge_bases.ingestion_sources.base import (\n    IngestionItemResult,\n    IngestionItemStatus,\n    IngestionRunStatus,\n    IngestionSummary,\n)\nfrom lfx.base.knowledge_bases.ingestion_sources.flow_component import FlowComponentSource\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.knowledge_bases.validation import (\n    is_valid_collection_name as is_valid_kb_collection_name,\n)\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.helpers.base_model import coalesce_bool\nfrom lfx.io import (\n    BoolInput,\n    DBProviderInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MessageTextInput,\n    ModelInput,\n    Output,\n    SecretStrInput,\n    StrInput,\n    TabInput,\n    TableInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n    session_scope,\n)\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n\ndef _inputs_for_mode(default_mode: str) -> list:\n    \"\"\"Return a fresh copy of the canonical inputs list with show flags set for the given mode.\n\n    Used by the legacy subclasses so a saved flow keyed on\n    ``KnowledgeIngestion`` / ``KnowledgeBase`` lands on a node template\n    whose default visibility already matches the pinned mode — no\n    \"wait for the user to click the mode tab\" UX gap on load.\n    \"\"\"\n    always_visible = set(KnowledgeComponent.default_keys) | set(KnowledgeComponent.mode_config[default_mode])\n    inputs_copy = []\n    for inp in KnowledgeComponent.inputs:\n        clone = inp.model_copy(deep=True) if hasattr(inp, \"model_copy\") else inp\n        if clone.name == \"mode\":\n            clone.value = default_mode\n        else:\n            clone.show = clone.name in always_visible\n        inputs_copy.append(clone)\n    return inputs_copy\n\n\nif TYPE_CHECKING:\n    from pathlib import Path\n\n# Mode constants. Plain-text labels (no emoji) for consistency with the rest of\n# Langflow's TabInput palette — see ``MemoryComponent`` for the same convention.\nMODE_INGEST = \"Ingest\"\nMODE_RETRIEVE = \"Retrieve\"\n\n# How often a flow ingestion checks whether its job was cancelled from the\n# Knowledge page while it waits on the embedding provider.\nJOB_CANCEL_POLL_SECONDS = 1.0\n\n\nclass KnowledgeIngestionCancelledError(Exception):\n    \"\"\"The ingestion's job was cancelled from the Knowledge page.\"\"\"\n\n\n_T = TypeVar(\"_T\")\n\n\nasync def _finish_despite_cancellation(awaitable: Awaitable[_T]) -> _T:\n    \"\"\"Run ``awaitable`` to completion even if the caller is cancelled meanwhile.\n\n    ``anyio.CancelScope(shield=True)`` holds off AnyIO cancellation, but a\n    repeated native ``Task.cancel()`` still interrupts it, and a stopped flow\n    build can cancel its task more than once. The work runs in its own task,\n    and the caller waits through every cancellation, re-raising once the work\n    has landed.\n    \"\"\"\n    task = asyncio.ensure_future(awaitable)\n    cancelled = False\n    with anyio.CancelScope(shield=True):\n        while not task.done():\n            try:\n                await asyncio.wait({task})\n            except asyncio.CancelledError:\n                cancelled = True\n    if cancelled:\n        error = None if task.cancelled() else task.exception()\n        if error is not None:\n            logger.warning(f\"Work finished during cancellation failed: {error!r}\")\n        raise asyncio.CancelledError from error\n    return task.result()\n\n\ndef _is_retrieve_mode(value: Any) -> bool:\n    \"\"\"Lenient mode check: treats any label containing 'Retrieve' as retrieve mode.\n\n    Older saved flows may carry the emoji-prefixed labels (\"📥 Ingest\" /\n    \"🔍 Retrieve\") this component used to ship with; substring matching\n    keeps those loading without forcing a flow rewrite.\n    \"\"\"\n    return isinstance(value, str) and \"Retrieve\" in value\n\n\n# Boolean flags of a ``column_config`` row. Toggled cells hold real booleans,\n# but a typed cell (or a flow saved while the cell rendered as plain text)\n# keeps the raw string, e.g. ``\"true\"`` — read them with ``coalesce_bool``.\n_COLUMN_FLAGS = (\"vectorize\", \"identifier\")\n\n\ndef _normalize_column_config(config_list: list[dict[str, Any]]) -> list[dict[str, Any]]:\n    \"\"\"Return copies of the rows with every flag as a real boolean.\"\"\"\n    return [{**row, **{key: coalesce_bool(row.get(key)) for key in _COLUMN_FLAGS}} for row in config_list]\n\n\n# Error message used by both the ingest and retrieve paths when the user is\n# running against an Astra cloud environment that disables these flows.\nastra_error_msg = \"Knowledge ingestion and retrieval are not supported in Astra cloud environment.\"\n\n_DEFAULT_OPENSEARCH_CONFIG = {\n    \"url_variable\": \"OPENSEARCH_URL\",\n    \"username_variable\": \"OPENSEARCH_USERNAME\",\n    \"password_variable\": \"OPENSEARCH_PASSWORD\",  # pragma: allowlist secret\n    \"index_name\": \"\",\n    \"vector_field\": \"vector_field\",\n    \"text_field\": \"text\",\n}\n\n\nclass KnowledgeComponent(Component):\n    \"\"\"One component for both writing into and reading from an OpenXFlow knowledge base.\n\n    A ``TabInput`` switches between ingestion and retrieval. The\n    ``update_build_config`` / ``update_outputs`` hooks hide the inputs\n    and the output that don't apply to the current mode so the canvas\n    node stays focused.\n    \"\"\"\n\n    display_name = \"Knowledge\"\n    description = \"Ingest into or retrieve from an OpenXFlow knowledge base.\"\n    icon = \"database\"\n    name = \"Knowledge\"\n\n    # ------ Mode → visible-fields wiring ---------------------------------\n    # ``default_keys`` are inputs always visible regardless of mode.\n    # ``mode_config`` lists the inputs unique to each mode; everything outside\n    # this set is hidden when its mode is not selected.\n    default_keys: list[str] = [\"mode\", \"knowledge_base\"]\n    mode_config: dict[str, list[str]] = {\n        MODE_INGEST: [\n            \"input_df\",\n            \"column_config\",\n            \"chunk_size\",\n            \"api_key\",\n            \"allow_duplicates\",\n            \"metadata_json\",\n        ],\n        MODE_RETRIEVE: [\n            \"search_query\",\n            \"top_k\",\n            \"include_metadata\",\n            \"include_embeddings\",\n            \"metadata_filter\",\n        ],\n    }\n\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self._cached_kb_path: Path | None = None\n        self._knowledge_output_result: Data | DataFrame | None = None\n\n    def _pre_run_setup(self) -> None:\n        self._knowledge_output_result = None\n\n    async def _get_output_result(self, output: Output):\n        # Both legacy handles dispatch to the selected mode. Terminal nodes\n        # evaluate both handles, but must execute the storage operation once.\n        if output.method not in {\"build_kb_info\", \"retrieve_data\"}:\n            return await super()._get_output_result(output)\n        if self._knowledge_output_result is None:\n            raw_output = output.model_copy(update={\"options\": None})\n            self._knowledge_output_result = await super()._get_output_result(raw_output)\n        output.value = output.apply_options(self._knowledge_output_result)\n        return output.value\n\n    @staticmethod\n    def _embedding_provider_from_selection(selection: Any) -> str | None:\n        \"\"\"Read a provider name from a raw or persisted model selection.\"\"\"\n        if isinstance(selection, list):\n            selection = selection[0] if selection else None\n        if not isinstance(selection, Mapping):\n            return None\n        provider = selection.get(\"provider\")\n        if not isinstance(provider, str) or not provider.strip():\n            return None\n        return provider.strip()\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Resolve the embedding provider before dialog hooks or runtime secrets.\n\n        New-KB configuration carries its ModelInput inside a dropdown dialog,\n        while existing KBs persist the provider in the KB row. Neither is a\n        top-level ModelInput, so both must be resolved explicitly before the\n        generic component pipeline hydrates ``api_key``.\n        \"\"\"\n        from lfx.base.models.provider_registry import resolve_provider_id\n        from lfx.services.model_provider_policy import ModelProviderPolicyError\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        knowledge_value = effective_parameters.get(\"knowledge_base\", getattr(self, \"knowledge_base\", None))\n\n        if isinstance(knowledge_value, Mapping):\n            if \"02_embedding_model\" not in knowledge_value:\n                return ()\n            provider = self._embedding_provider_from_selection(knowledge_value.get(\"02_embedding_model\"))\n        elif isinstance(knowledge_value, str) and knowledge_value.strip():\n            metadata = await self._get_kb_metadata(knowledge_value.strip())\n            provider = self._embedding_provider_from_selection(metadata.get(\"model_selection\"))\n            if provider is None:\n                legacy_provider = metadata.get(\"embedding_provider\")\n                provider = legacy_provider.strip() if isinstance(legacy_provider, str) else None\n                if provider == \"Unknown\":\n                    provider = None\n        else:\n            return ()\n\n        if provider is None:\n            unknown_provider = \"unknown\"\n            raise ModelProviderPolicyError(unknown_provider, purpose)\n        return (resolve_provider_id(provider),)\n\n    @dataclass\n    class NewKnowledgeBaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"name\": \"create_knowledge_base\",\n                        \"description\": \"Create new knowledge in OpenXFlow.\",\n                        \"display_name\": \"Create new Knowledge Base\",\n                        \"field_order\": [\n                            \"01_new_kb_name\",\n                            \"02_embedding_model\",\n                            \"03_knowledge_backend\",\n                        ],\n                        \"template\": {\n                            \"01_new_kb_name\": StrInput(\n                                name=\"new_kb_name\",\n                                display_name=\"Knowledge Name\",\n                                info=\"Name of the new knowledge to create.\",\n                                required=True,\n                            ),\n                            \"02_embedding_model\": ModelInput(\n                                name=\"embedding_model\",\n                                display_name=\"Choose Embedding Model\",\n                                info=(\n                                    \"Select the embedding model to use for this knowledge base. \"\n                                    \"OpenXFlow uses the configured credentials for that model provider.\"\n                                ),\n                                required=True,\n                                model_type=\"embedding\",\n                            ),\n                            \"03_knowledge_backend\": DBProviderInput(\n                                name=\"knowledge_backend\",\n                                display_name=\"DB Provider\",\n                                info=(\n                                    \"Select where this knowledge base stores vectors. \"\n                                    \"OpenSearch uses the global DB Providers settings.\"\n                                ),\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    # ------ Inputs --------------------------------------------------------\n    # ``knowledge_base`` is kept at position 0 so legacy tests that check\n    # ``component.inputs[0].dialog_inputs`` continue to work.\n    inputs = [\n        DropdownInput(\n            name=\"knowledge_base\",\n            display_name=\"Knowledge\",\n            info=\"Select the knowledge to load data from.\",\n            required=True,\n            options=[],\n            refresh_button=True,\n            real_time_refresh=True,\n            dialog_inputs=asdict(NewKnowledgeBaseInput()),\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[MODE_INGEST, MODE_RETRIEVE],\n            value=MODE_INGEST,\n            info=\"Switch between writing new data into the knowledge base and querying it.\",\n            real_time_refresh=True,\n            tool_mode=True,\n        ),\n        # --- Ingest-only inputs (default-shown; hidden when mode == Retrieve) -\n        HandleInput(\n            name=\"input_df\",\n            display_name=\"Input\",\n            info=(\n                \"Table with all original columns (already chunked / processed). \"\n                \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n                \"it is converted to a DataFrame automatically.\"\n            ),\n            input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n            required=True,\n            dynamic=True,\n            show=True,\n        ),\n        TableInput(\n            name=\"column_config\",\n            display_name=\"Column Configuration\",\n            info=\"Configure column behavior for the knowledge base.\",\n            required=True,\n            table_schema=[\n                {\n                    \"name\": \"column_name\",\n                    \"display_name\": \"Column Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Name of the column in the source DataFrame\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"vectorize\",\n                    \"display_name\": \"Vectorize\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Create embeddings for this column\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"identifier\",\n                    \"display_name\": \"Identifier\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Use this column as unique identifier\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"column_name\": \"text\",\n                    \"vectorize\": True,\n                    \"identifier\": True,\n                },\n            ],\n            dynamic=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"Batch size for processing embeddings\",\n            advanced=True,\n            value=1000,\n            dynamic=True,\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Embedding Provider API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        BoolInput(\n            name=\"allow_duplicates\",\n            display_name=\"Allow Duplicates\",\n            info=\"Allow duplicate rows in the knowledge base\",\n            advanced=True,\n            value=False,\n            dynamic=True,\n            show=True,\n        ),\n        StrInput(\n            name=\"metadata_json\",\n            display_name=\"Metadata\",\n            info=(\n                \"Optional JSON object of user metadata applied to every chunk produced by this \"\n                'run (e.g. {\"tag\": \"invoice\", \"year\": \"2026\"}). Same shape as the upload modal '\n                \"Metadata section so chunks browser filters + Knowledge retrieval metadata_filter \"\n                \"work uniformly across upload, folder, and flow-driven ingestion. Malformed JSON is \"\n                \"ignored with a warning rather than failing the run.\"\n            ),\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        # --- Retrieve-only inputs (default-hidden; shown when mode == Retrieve) -\n        MessageTextInput(\n            name=\"search_query\",\n            display_name=\"Search Query\",\n            info=\"Optional search query to filter knowledge base data.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of top results to return from the knowledge base.\",\n            value=5,\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_metadata\",\n            display_name=\"Include Metadata\",\n            info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n            value=True,\n            advanced=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_embeddings\",\n            display_name=\"Include Embeddings\",\n            info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n            value=False,\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"metadata_filter\",\n            display_name=\"Metadata Filter\",\n            info=(\n                \"Optional JSON object of user-metadata key/value pairs. Only chunks \"\n                'whose source_metadata matches every key are returned (e.g. {\"tag\": \"invoice\"} '\n                'or {\"tag\": [\"invoice\", \"audit\"]} for OR-of-values). Backends without '\n                \"native filtering apply the match client-side after retrieval.\"\n            ),\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n    ]\n\n    # ------ Outputs -------------------------------------------------------\n    # Both outputs are declared at the class level so the runtime can\n    # dispatch to either method depending on which one the saved flow has\n    # wired up. ``update_outputs`` filters the canvas-visible output per\n    # selected mode; see ``TypeConverterComponent`` and ``MemoryComponent``\n    # for the same pattern.\n    #\n    # Output names match the legacy ``KnowledgeIngestionComponent``\n    # (``dataframe_output``) and ``KnowledgeBaseComponent``\n    # (``retrieve_data``) so saved flow edges keyed on those names resolve\n    # cleanly against the merged component.\n    outputs = [\n        Output(\n            display_name=\"Results\",\n            name=\"dataframe_output\",\n            method=\"build_kb_info\",\n            types=[\"JSON\"],\n            selected=\"JSON\",\n        ),\n        Output(\n            display_name=\"Results\",\n            name=\"retrieve_data\",\n            method=\"retrieve_data\",\n            info=\"Returns the data from the selected knowledge base.\",\n            types=[\"Table\"],\n            selected=\"Table\",\n        ),\n    ]\n\n    # ------ Mode-driven UI updates ---------------------------------------\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"Sync the visible output with the current ``mode`` value on canvas load.\n\n        Saved flows hit this path: ``update_outputs`` is normally only triggered\n        by ``real_time_refresh`` field edits, so without this re-sync the\n        canvas could land with both outputs visible.\n        \"\"\"\n        await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = new_frontend_node.get(\"template\", {})\n        mode_value = template.get(\"mode\", {}).get(\"value\", MODE_INGEST)\n        self.update_outputs(new_frontend_node, \"mode\", mode_value)\n        # The rebuilt template carries class-default (Ingest) ``show`` flags,\n        # so a saved Retrieve node returned ``search_query`` hidden and the canvas\n        # silently dropped the edge feeding it.\n        self._apply_mode_visibility(template)\n        return new_frontend_node\n\n    def _apply_mode_visibility(self, build_config, field_name: str | None = None, field_value: Any = None):\n        \"\"\"Show only the inputs that belong to the effective mode.\"\"\"\n        current_mode = build_config.get(\"mode\", {}).get(\"value\") if isinstance(build_config, dict) else None\n        if field_name == \"mode\":\n            current_mode = field_value\n        # Map legacy/emoji-prefixed labels onto the current canonical values so\n        # flows saved before the label change still toggle visibility correctly.\n        if _is_retrieve_mode(current_mode):\n            current_mode = MODE_RETRIEVE\n        elif current_mode not in self.mode_config:\n            current_mode = MODE_INGEST\n        return set_current_fields(\n            build_config=build_config if isinstance(build_config, dotdict) else dotdict(build_config),\n            action_fields=self.mode_config,\n            selected_action=current_mode,\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Filter visible outputs to match the selected mode.\n\n        Triggered by the ``mode`` ``TabInput`` ``real_time_refresh`` flag.\n        The class-level ``outputs`` list always carries both entries so the\n        runtime can resolve either ``build_kb_info`` or ``retrieve_data``\n        regardless of canvas visibility — we just hide the unused one here.\n        \"\"\"\n        if field_name != \"mode\":\n            return frontend_node\n        if _is_retrieve_mode(field_value):\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"retrieve_data\",\n                    method=\"retrieve_data\",\n                    info=\"Returns the data from the selected knowledge base.\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                )\n            ]\n        else:\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"dataframe_output\",\n                    method=\"build_kb_info\",\n                    types=[\"JSON\"],\n                    selected=\"JSON\",\n                )\n            ]\n        return frontend_node\n\n    async def update_build_config(\n        self,\n        build_config,\n        field_value: Any,\n        field_name: str | None = None,\n    ):\n        \"\"\"Refresh KB options, drive the create-KB dialog, and hide off-mode fields.\"\"\"\n        # Astra-cloud gate covers both ingest and retrieve paths.\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        # Always populate the create-KB dialog's embedding-model options so the\n        # ModelInput renders correctly regardless of which input triggered the\n        # refresh.\n        try:\n            dialog_template = (\n                build_config[\"knowledge_base\"]\n                .get(\"dialog_inputs\", {})\n                .get(\"fields\", {})\n                .get(\"data\", {})\n                .get(\"node\", {})\n                .get(\"template\", {})\n            )\n            if \"02_embedding_model\" in dialog_template:\n                embedding_options = get_embedding_model_options(user_id=self.user_id)\n                dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n        except Exception:  # noqa: BLE001\n            self.log(\"Failed to populate embedding model options in dialog\")\n\n        # KB-picker refresh + create-new-KB flow (lifted from the legacy ingestion\n        # component verbatim; relied on by both the canvas refresh button and the\n        # dialog-submit path).\n        if field_name == \"knowledge_base\":\n            # Lazy import keeps lfx importable without langflow installed.\n            from langflow.services.database.models.user.crud import get_user_by_id\n\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for fetching knowledge base list.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n                if not current_user:\n                    msg = f\"User with ID {self.user_id} not found.\"\n                    raise ValueError(msg)\n            if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n                model_selection = field_value[\"02_embedding_model\"]\n                if isinstance(model_selection, dict):\n                    model_selection = [model_selection]\n\n                backend_type, backend_config = self._normalize_backend_selection(\n                    field_value.get(\"03_knowledge_backend\")\n                )\n                ensure_storage_routing_allowed(backend_config, is_superuser=bool(current_user.is_superuser))\n                new_kb_name = field_value[\"01_new_kb_name\"]\n                from langflow.api.utils.kb_helpers import local_chroma_rejection_reason, validate_kb_name\n\n                validate_kb_name(new_kb_name)\n                rejection = local_chroma_rejection_reason(backend_type, backend_config)\n                if rejection:\n                    raise ValueError(rejection)\n\n                embed_model = get_embeddings(\n                    model=model_selection,\n                    user_id=self.user_id,\n                )\n\n                try:\n                    await asyncio.wait_for(\n                        asyncio.to_thread(embed_model.embed_query, \"test\"),\n                        timeout=10,\n                    )\n                except TimeoutError as e:\n                    msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n                    raise ValueError(msg) from e\n                except Exception as e:\n                    msg = f\"Embedding validation failed: {e!s}\"\n                    raise ValueError(msg) from e\n\n                build_config[\"knowledge_base\"][\"value\"] = new_kb_name\n                await self._create_knowledge_base_record(\n                    user_id=self.user_id,\n                    name=field_value[\"01_new_kb_name\"],\n                    model_selection=model_selection,\n                    backend_type=backend_type,\n                    backend_config=backend_config,\n                )\n\n            build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(user_id=self.user_id)\n            if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n                build_config[\"knowledge_base\"][\"value\"] = None\n\n        # Honor the current mode regardless of which field triggered the refresh.\n        return self._apply_mode_visibility(build_config, field_name, field_value)\n\n    # =====================================================================\n    #                       INGESTION CODE PATH\n    # =====================================================================\n    @staticmethod\n    def _scalar_notna(value) -> bool:\n        \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n        ``pd.notna`` returns an array when given an array-like input, which\n        cannot be used directly in a boolean context.  This helper collapses\n        the result to a single scalar ``bool``.\n        \"\"\"\n        result = pd.notna(value)\n        if hasattr(result, \"__iter__\") and not isinstance(result, str):\n            import numpy as np\n\n            arr = np.asarray(result)\n            return arr.size > 0 and arr.all()\n        return bool(result)\n\n    def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n        \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n        if not self.column_config:\n            msg = \"Column configuration cannot be empty\"\n            raise ValueError(msg)\n\n        config_list = self.column_config if isinstance(self.column_config, list) else []\n\n        df_columns = set(df_source.columns)\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            if col_name not in df_columns:\n                msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n                raise ValueError(msg)\n\n        return config_list\n\n    @staticmethod\n    def _extract_source_types_from_df(df_source: pd.DataFrame) -> set[str]:\n        \"\"\"Pull file extensions out of common path/name columns on the source DataFrame.\n\n        The direct-upload ingestion path stores extensions in\n        the KB row's ``source_types`` so the KB list can render the\n        correct file-type icon. When ingestion happens via a connected\n        ``input_df`` (e.g. File → Knowledge) the same field stayed empty and\n        the icon defaulted to a blank tile. We look at the well-known columns\n        the File / S3 / cloud-storage components produce and collect any\n        plausible extension so the icon is consistent across both flows.\n        \"\"\"\n        candidate_columns = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for col in candidate_columns:\n            if col not in df_source.columns:\n                continue\n            for value in df_source[col].dropna():\n                ext = KnowledgeComponent._extension_from_value(value)\n                # Drop anything that doesn't look like an extension (URL\n                # query strings, version segments, etc.) — the icon palette\n                # keys off short alphanumeric tokens like \"pdf\"/\"docx\".\n                if ext:\n                    extensions.add(ext)\n        return extensions\n\n    @staticmethod\n    def _extension_from_value(value: Any) -> str | None:\n        \"\"\"Return a normalized file-extension token from a path / filename / MIME string.\n\n        Accepts values like ``\"report.PDF\"``, ``\"/docs/notes.txt\"``, or\n        ``\"application/pdf\"`` and returns ``\"pdf\"`` / ``\"txt\"``. Returns\n        ``None`` if no plausible extension can be derived.\n        \"\"\"\n        extension_length_limit = 10\n        if value is None:\n            return None\n        text = str(value).strip()\n        if not text:\n            return None\n        # MIME types like ``application/pdf`` carry the canonical extension\n        # in the subtype slot — preserve them so File / S3 messages keyed\n        # only on ``mimetype`` still resolve to an icon.\n        if \"/\" in text and \".\" not in text.rsplit(\"/\", 1)[-1]:\n            subtype = text.rsplit(\"/\", 1)[-1].strip().lower()\n            return subtype if subtype and len(subtype) <= extension_length_limit and subtype.isalnum() else None\n        if \".\" not in text:\n            return None\n        ext = text.rsplit(\".\", 1)[-1].strip().lower()\n        if ext and len(ext) <= extension_length_limit and ext.isalnum():\n            return ext\n        return None\n\n    @classmethod\n    def _extract_source_types_from_mapping(cls, mapping: Any) -> set[str]:\n        \"\"\"Pull extensions from a Message/Data-style ``data`` dict or a plain dict.\"\"\"\n        if not isinstance(mapping, dict):\n            return set()\n        candidate_keys = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for key in candidate_keys:\n            ext = cls._extension_from_value(mapping.get(key))\n            if ext:\n                extensions.add(ext)\n        return extensions\n\n    @classmethod\n    def _extract_source_types_from_input(cls, input_value: Any) -> set[str]:\n        \"\"\"Pull file extensions out of the raw component input.\n\n        ``convert_to_dataframe`` strips Message/Data fields down to ``text``\n        when projecting onto a DataFrame, so file metadata attached to a\n        File-component \"Raw Content\" output never reaches\n        ``_extract_source_types_from_df``. Looking at the raw input first\n        keeps the KB icon consistent with direct upload.\n        \"\"\"\n        if input_value is None:\n            return set()\n        if isinstance(input_value, list):\n            extensions: set[str] = set()\n            for item in input_value:\n                extensions |= cls._extract_source_types_from_input(item)\n            return extensions\n        if isinstance(input_value, pd.DataFrame):\n            return cls._extract_source_types_from_df(input_value)\n        # Message / Data / JSON all expose a ``data`` dict via the lfx schema.\n        mapping = getattr(input_value, \"data\", None)\n        if mapping is None and isinstance(input_value, dict):\n            mapping = input_value\n        return cls._extract_source_types_from_mapping(mapping)\n\n    async def _refresh_kb_stats(\n        self,\n        *,\n        kb_record_id: Any,\n        backend: BaseVectorStoreBackend,\n        extensions: set[str],\n    ) -> None:\n        \"\"\"Recount the KB from its vector store and write the totals onto the row.\n\n        Backend-agnostic: counts come from ``count`` / ``iter_documents`` /\n        ``storage_size_bytes``, so a Chroma, OpenSearch, or pgVector KB all report\n        real numbers. ``extensions`` are merged into ``source_types`` so the KB\n        list renders the right file-type icon regardless of which ingestion route\n        produced the chunks.\n\n        Best-effort: the chunks are already written and committed by the time this\n        runs, so a stats refresh failure must not fail the ingestion.\n        \"\"\"\n        if kb_record_id is None:\n            return\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return\n\n        try:\n            chunks = await backend.count()\n            characters = 0\n            words = 0\n            async with aclosing(backend.iter_documents()) as batches:\n                async for batch in batches:\n                    for document in batch:\n                        characters += len(document.content)\n                        words += len(document.content.split())\n\n            record = await knowledge_base_service.get_by_id(kb_record_id)\n            existing = set(record.source_types or []) if record is not None else set()\n\n            await knowledge_base_service.update_stats(\n                kb_record_id,\n                chunks=chunks,\n                words=words,\n                characters=characters,\n                size_bytes=await backend.storage_size_bytes(),\n                source_types=sorted(existing | extensions),\n            )\n        except Exception as e:  # noqa: BLE001 — stats are cosmetic; chunks are already written\n            self.log(f\"Warning: Could not refresh knowledge base stats: {e}\")\n\n    @staticmethod\n    def _default_backend_selection() -> tuple[str, dict[str, Any]]:\n        \"\"\"Deployment default for a new KB with no explicit backend chosen.\n\n        Environment-driven: pgVector when ``PGVECTOR_CONNECTION_STRING`` is set,\n        else OpenSearch when ``OPENSEARCH_URL`` is set, else SQLite. An\n        unspecified selection (headless flows, multi-replica) therefore lands on\n        the deployment's shared store.\n        \"\"\"\n        from lfx.base.knowledge_bases.backends.postgres import resolve_default_kb_backend\n\n        backend_type = resolve_default_kb_backend()\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, dict(_DEFAULT_OPENSEARCH_CONFIG)\n        return backend_type, {}\n\n    @classmethod\n    def _normalize_backend_selection(cls, value: Any) -> tuple[str, dict[str, Any]]:\n        \"\"\"Normalize a DBProviderInput value into backend type/config.\"\"\"\n        if not value:\n            return cls._default_backend_selection()\n\n        backend_config: dict[str, Any]\n        if isinstance(value, str):\n            backend_type, backend_config = value, {}\n        elif isinstance(value, dict):\n            backend_type = str(value.get(\"backend_type\") or value.get(\"id\") or BackendType.SQLITE.value)\n            backend_config = value.get(\"backend_config\") or value.get(\"config\") or {}\n        else:\n            msg = \"Invalid vector-store backend selection.\"\n            raise TypeError(msg)\n        if not isinstance(backend_config, dict):\n            msg = \"Vector-store backend configuration must be an object.\"\n            raise TypeError(msg)\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, {**_DEFAULT_OPENSEARCH_CONFIG, **backend_config}\n        if backend_type in {BackendType.SQLITE.value, BackendType.POSTGRES.value}:\n            return backend_type, backend_config\n        msg = \"Chroma is retired. Choose SQLite, pgVector, or OpenSearch for a new knowledge base.\"\n        raise ValueError(msg)\n\n    async def _create_knowledge_base_record(\n        self,\n        *,\n        user_id: Any,\n        name: str,\n        model_selection: list[dict[str, Any]],\n        backend_type: str,\n        backend_config: dict[str, Any],\n    ) -> None:\n        \"\"\"Persist the component-created KB in the DB when Langflow is available.\"\"\"\n        from langflow.api.utils import knowledge_base_service\n\n        await knowledge_base_service.create_record(\n            user_id=user_id,\n            name=name,\n            model_selection=model_selection,\n            column_config=_normalize_column_config(self.column_config) if isinstance(self.column_config, list) else [],\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n        \"\"\"Build detailed column metadata.\"\"\"\n        metadata: dict[str, Any] = {\n            \"total_columns\": len(df_source.columns),\n            \"mapped_columns\": len(config_list),\n            \"unmapped_columns\": len(df_source.columns) - len(config_list),\n            \"columns\": [],\n            \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n        }\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            vectorize = coalesce_bool(config.get(\"vectorize\"))\n            identifier = coalesce_bool(config.get(\"identifier\"))\n\n            metadata[\"columns\"].append(\n                {\n                    \"name\": col_name,\n                    \"vectorize\": vectorize,\n                    \"identifier\": identifier,\n                }\n            )\n\n            if vectorize:\n                metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n            if identifier:\n                metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n        return metadata\n\n    async def _create_vector_store(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        \"\"\"Create vector store using the configured DB provider.\"\"\"\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            return await self._ingest_into_backend(backend, df_source, config_list, embedding_function, job_id=job_id)\n        except BaseException:\n            await _finish_despite_cancellation(backend.teardown())\n            raise\n\n    async def _ingest_into_backend(\n        self,\n        backend: BaseVectorStoreBackend,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        await backend.ensure_ready()\n\n        from langflow.services.knowledge_base_storage.runtime import operation, resolve_record\n\n        record = await resolve_record(self._user_uuid, self.knowledge_base)\n        async with operation(record, shared=True):\n            existing_ids = set()\n            if not self.allow_duplicates:\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        for document in batch:\n                            doc_id = document.metadata.get(\"_id\")\n                            if doc_id:\n                                existing_ids.add(doc_id)\n\n        data_objects = await self._convert_df_to_data_objects(df_source, config_list, existing_ids=existing_ids)\n\n        user_metadata_tag = self._resolve_user_metadata_tag()\n\n        documents = []\n        for data_obj in data_objects:\n            doc = data_obj.to_lc_document()\n            if user_metadata_tag:\n                doc.metadata[\"source_metadata\"] = user_metadata_tag\n            documents.append(doc)\n        if not documents:\n            return backend\n\n        # No storage lease may surround the provider call. The write lease\n        # below rechecks routing and IDs after concurrent operations finish.\n        vectors = await self._embed_unless_job_cancelled(\n            embedding_function, [doc.page_content for doc in documents], job_id\n        )\n        if len(vectors) != len(documents):\n            msg = \"Embedding provider returned an incorrect number of vectors\"\n            raise BackendConfigurationError(msg)\n        embedded = [\n            IngestedDocument(doc.page_content, doc.metadata, vector, id=doc.id)\n            for doc, vector in zip(documents, vectors, strict=True)\n        ]\n        async with operation(record):\n            # A cancel that landed after the embeddings returned must still\n            # keep this run's chunks out of the KB.\n            await self._raise_if_job_cancelled(job_id)\n            if not self.allow_duplicates:\n                current_ids = set()\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        current_ids.update(doc.metadata[\"_id\"] for doc in batch if doc.metadata.get(\"_id\"))\n                unique = []\n                for doc in embedded:\n                    doc_id = doc.metadata.get(\"_id\")\n                    if doc_id and doc_id in current_ids:\n                        continue\n                    if doc_id:\n                        current_ids.add(doc_id)\n                    unique.append(doc)\n                embedded = unique\n\n            if embedded:\n                await backend.add_embedded_documents(embedded)\n                self.log(f\"Added {len(embedded)} documents to vector store '{self.knowledge_base}'\")\n\n        return backend\n\n    async def _embed_unless_job_cancelled(\n        self, embedding_function, texts: list[str], job_id: uuid.UUID | None\n    ) -> list[list[float]]:\n        \"\"\"Embed ``texts``, giving up as soon as the run's job is cancelled.\n\n        The Knowledge page cancels a run by marking its job. The embedding call\n        is the long, write-free part of a flow ingestion, so it is the safe\n        place to stop.\n        \"\"\"\n        await self._raise_if_job_cancelled(job_id)\n        if job_id is None:\n            return await embedding_function.aembed_documents(texts)\n\n        # A task group would wrap provider errors in an ExceptionGroup, so a\n        # plain watcher task cancels this scope instead.\n        with anyio.CancelScope() as scope:\n            watcher = asyncio.ensure_future(self._cancel_when_job_cancelled(job_id, scope))\n            try:\n                return await embedding_function.aembed_documents(texts)\n            finally:\n                # The watcher may be mid-poll; let its DB session close.\n                watcher.cancel()\n                with anyio.CancelScope(shield=True):\n                    await asyncio.wait({watcher})\n        raise self._cancelled_error()\n\n    async def _cancel_when_job_cancelled(self, job_id: uuid.UUID, scope: anyio.CancelScope) -> None:\n        # The cancel is a job-row write from another request, so it is polled.\n        while True:\n            await asyncio.sleep(JOB_CANCEL_POLL_SECONDS)\n            if await self._job_cancelled(job_id):\n                scope.cancel()\n                return\n\n    async def _raise_if_job_cancelled(self, job_id: uuid.UUID | None) -> None:\n        if await self._job_cancelled(job_id):\n            raise self._cancelled_error()\n\n    def _cancelled_error(self) -> KnowledgeIngestionCancelledError:\n        return KnowledgeIngestionCancelledError(f\"Ingestion into '{self.knowledge_base}' was cancelled.\")\n\n    async def _job_cancelled(self, job_id: uuid.UUID | None) -> bool:\n        if job_id is None:\n            return False\n        try:\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError:\n            return False\n        try:\n            job = await get_job_service().get_job_by_job_id(job_id)\n        except Exception as exc:  # noqa: BLE001 — a failed poll must not abort the ingestion\n            self.log(f\"Could not check whether ingestion job {job_id} was cancelled: {exc}\")\n            return False\n        return job is not None and job.status == JobStatus.CANCELLED\n\n    async def _convert_df_to_data_objects(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        existing_ids: set[str] | None = None,\n    ) -> list[Data]:\n        \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n        data_objects: list[Data] = []\n\n        if existing_ids is None:\n            existing_ids = set()\n\n        content_cols = []\n        identifier_cols = []\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n\n            if coalesce_bool(config.get(\"vectorize\")):\n                content_cols.append(col_name)\n            if coalesce_bool(config.get(\"identifier\")):\n                identifier_cols.append(col_name)\n\n        for _, row in df_source.iterrows():\n            identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n            page_content = \" \".join(identifier_parts)\n\n            data_dict = {\n                \"text\": page_content,\n            }\n\n            if identifier_cols:\n                identifier_parts = [\n                    str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n                ]\n                page_content = \" \".join(identifier_parts)\n\n            for col in df_source.columns:\n                if col not in content_cols and col in row and self._scalar_notna(row[col]):\n                    value = row[col]\n                    data_dict[col] = str(value)\n\n            page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n            data_dict[\"_id\"] = page_content_hash\n\n            if not self.allow_duplicates and page_content_hash in existing_ids:\n                self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n                continue\n\n            data_obj = Data(data=data_dict)\n            data_objects.append(data_obj)\n\n        return data_objects\n\n    def is_valid_collection_name(self, name: str) -> bool:\n        \"\"\"Return whether ``name`` satisfies the shared collection-name contract.\"\"\"\n        return is_valid_kb_collection_name(name)\n\n    def _resolve_store_path(\n        self,\n        kb_user: str,\n        backend_type: str,\n        backend_config: dict[str, Any] | None,\n    ) -> Path | None:\n        \"\"\"Return this KB's on-disk directory, or ``None`` when it needs no disk.\n\n        Only local Chroma stores anything on this box; every other backend\n        resolves to ``None`` and never touches the filesystem.\n        \"\"\"\n        # Lazy import keeps lfx importable without langflow's DB services.\n        from langflow.api.utils.kb_helpers import resolve_local_store_path\n\n        return resolve_local_store_path(\n            self.knowledge_base,\n            kb_user,\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    async def _kb_username(self) -> str:\n        \"\"\"Resolve the owning user's username (used to root a local-Chroma path).\"\"\"\n        cached = getattr(self, \"_cached_kb_username\", None)\n        if cached is not None:\n            return cached\n\n        # Lazy import to keep ``lfx`` importable standalone — langflow's\n        # user/DB models are not always available at module load time.\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        async with session_scope() as db:\n            if not self.user_id:\n                msg = \"User ID is required for fetching knowledge base path.\"\n                raise ValueError(msg)\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n            self._cached_kb_username = current_user.username\n\n        return self._cached_kb_username\n\n    async def _kb_path(self) -> Path | None:\n        \"\"\"Resolve the KB's local directory from its configured backend.\"\"\"\n        cached_path = getattr(self, \"_cached_kb_path\", None)\n        if cached_path is not None:\n            return cached_path\n\n        backend_type, backend_config = await self._resolve_backend_config()\n        kb_user = await self._kb_username()\n        self._cached_kb_path = self._resolve_store_path(kb_user, backend_type, backend_config)\n        return self._cached_kb_path\n\n    def _resolve_user_metadata_tag(self) -> str:\n        \"\"\"Return the JSON-encoded user metadata tag for chunk writes.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return \"\"\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return \"\"\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError) as exc:\n            self.log(f\"KnowledgeComponent: metadata_json is not valid JSON ({exc}); skipping metadata stamp.\")\n            return \"\"\n        if not isinstance(decoded, dict):\n            self.log(\"KnowledgeComponent: metadata_json must decode to a JSON object; skipping metadata stamp.\")\n            return \"\"\n        return json.dumps(decoded, sort_keys=True)\n\n    async def build_kb_info(self) -> Data:\n        \"\"\"Main ingestion routine → returns a dict with KB metadata.\n\n        The annotation is intentionally narrowed to ``Data`` even though the\n        cross-mode fallback below may return a ``DataFrame`` from\n        ``retrieve_data``. The frontend builds React-Flow handle IDs from\n        this output's type list; widening it to ``Data | DataFrame`` makes\n        the API advertise ``[\"JSON\", \"Table\"]`` for the ingest output and\n        breaks every saved-edge sourceHandle that was generated against\n        a single-type handle (BUG-02). Python doesn't enforce return\n        annotations at runtime, so the rare fallback path keeps working.\n        \"\"\"\n        if _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.retrieve_data()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        run_id: uuid.UUID | None = None\n        run_job_id: uuid.UUID | None = None\n        run_summary: IngestionSummary | None = None\n        run_status: IngestionRunStatus = IngestionRunStatus.SUCCEEDED\n        run_error: str | None = None\n        kb_record_id: uuid.UUID | None = None\n        previous_kb_status = \"ready\"\n        previous_failure_reason: str | None = None\n        try:\n            input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n            df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n            config_list = self._validate_column_config(df_source)\n            column_metadata = self._build_column_metadata(config_list, df_source)\n\n            # Embedding config comes from the ``knowledge_base`` row — the sole\n            # authority. No sidecar is read, so ingestion resolves the same model\n            # on any replica.\n            stored_metadata = await self._get_kb_metadata()\n            # A cancelled run restores this status. Only an earlier failure is\n            # kept; \"empty\" is an API projection of a ready KB with no chunks.\n            if stored_metadata.get(\"status\") == \"failed\":\n                previous_kb_status = \"failed\"\n                previous_failure_reason = stored_metadata.get(\"failure_reason\")\n            model_selection = stored_metadata.get(\"model_selection\")\n            if model_selection:\n                model_selection = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            else:\n                embedding_model_name = stored_metadata.get(\"embedding_model\")\n                embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n                if embedding_model_name:\n                    try:\n                        all_options = get_embedding_model_options(user_id=self.user_id)\n                        match = next(\n                            (o for o in all_options if o.get(\"name\") == embedding_model_name),\n                            None,\n                        )\n                        if match:\n                            model_selection = [match]\n                        else:\n                            self.log(\n                                f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n                                \"from the knowledge base record is no longer available in the model registry. \"\n                                \"Please re-create this knowledge base with a supported embedding model.\"\n                            )\n                            msg = (\n                                f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n                                \"The knowledge base was created with an older format and the model \"\n                                \"is not available in the current registry. \"\n                                \"Please re-create the knowledge base with a supported embedding model.\"\n                            )\n                            raise ValueError(msg)\n                    except ValueError:\n                        raise\n                    except Exception:  # noqa: BLE001\n                        self.log(\n                            f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n                            \"Please re-create this knowledge base with a supported embedding model.\"\n                        )\n                        msg = (\n                            f\"Could not look up embedding model '{embedding_model_name}' \"\n                            f\"(provider: {embedding_provider}). \"\n                            \"Please re-create the knowledge base with a supported embedding model.\"\n                        )\n                        raise ValueError(msg)  # noqa: B904\n\n            if not model_selection:\n                msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n                raise ValueError(msg)\n\n            # ``api_key=None`` falls through to the user's variables table and\n            # then the environment inside ``get_embeddings`` — the same\n            # resolution the server-side ingestion path uses. The component input\n            # only overrides it.\n            embedding_function = get_embeddings(\n                model=model_selection,\n                user_id=self.user_id,\n                api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n                chunk_size=self.chunk_size,\n            )\n\n            kb_path = await self._kb_path()\n\n            run_id, run_job_id, run_summary, kb_record_id = await self._begin_ingestion_run(kb_path)\n            if kb_record_id is not None:\n                await self._record_kb_status(kb_record_id, \"ingesting\")\n\n            backend = await self._create_vector_store(\n                df_source, config_list, embedding_function=embedding_function, job_id=run_job_id\n            )\n\n            try:\n                # Stamp the KB with the file extensions we just ingested so\n                # the Knowledge Bases list renders the correct icon for\n                # flow-driven ingestion (input_df), matching direct upload.\n                # We look at the raw input first because ``convert_to_dataframe``\n                # drops Message/Data metadata fields (file_path, mimetype, …)\n                # when projecting onto the DataFrame.\n                source_types = self._extract_source_types_from_input(input_value)\n                source_types |= self._extract_source_types_from_df(df_source)\n                # Storage routing returns a guarded proxy with the backend's\n                # methods, so a concrete-class check would skip the refresh.\n                await self._refresh_kb_stats(\n                    kb_record_id=kb_record_id,\n                    backend=backend,\n                    extensions=source_types,\n                )\n            finally:\n                await _finish_despite_cancellation(backend.teardown())\n\n            meta: dict[str, Any] = {\n                \"kb_id\": str(uuid.uuid4()),\n                \"kb_name\": self.knowledge_base,\n                \"rows\": len(df_source),\n                \"column_metadata\": column_metadata,\n                \"path\": str(kb_path),\n                \"config_columns\": len(config_list),\n                \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n            }\n\n            if run_summary is not None:\n                run_summary.record_item(\n                    IngestionItemResult(\n                        item_id=self.knowledge_base,\n                        display_name=f\"{self.knowledge_base} ({len(df_source)} rows)\",\n                        status=IngestionItemStatus.SUCCEEDED,\n                        chunks_created=len(df_source),\n                    ),\n                )\n\n            if kb_record_id is not None:\n                # Stats were already refreshed straight from the backend above.\n                await self._record_kb_status(kb_record_id, \"ready\")\n\n            self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n            return Data(data=meta)\n\n        except asyncio.CancelledError:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = \"Flow ingestion was cancelled.\"\n            raise\n        except KnowledgeIngestionCancelledError as e:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = str(e)\n            raise\n        except (OSError, ValueError, RuntimeError, KeyError) as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            msg = f\"Error during KB ingestion: {e}\"\n            raise RuntimeError(msg) from e\n        except Exception as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            raise\n        finally:\n\n            async def settle_run() -> None:\n                if run_id is not None and run_summary is not None:\n                    await self._finalize_ingestion_run(\n                        run_id=run_id,\n                        job_id=run_job_id,\n                        summary=run_summary,\n                        status=run_status,\n                        error_message=run_error,\n                    )\n                if kb_record_id is not None:\n                    if run_status is IngestionRunStatus.FAILED:\n                        await self._record_kb_status(kb_record_id, \"failed\", failure_reason=run_error)\n                    elif run_status is IngestionRunStatus.CANCELLED:\n                        await self._record_kb_status(\n                            kb_record_id, previous_kb_status, failure_reason=previous_failure_reason\n                        )\n\n            # A stopped build can cancel this task more than once, and the\n            # writes that make the run terminal must survive every cancel.\n            await _finish_despite_cancellation(settle_run())\n\n    async def _begin_ingestion_run(\n        self,\n        kb_path: Path,\n    ) -> tuple[uuid.UUID | None, uuid.UUID | None, IngestionSummary | None, uuid.UUID | None]:\n        \"\"\"Create a parent ``Job`` and seed an ingestion-run row.\"\"\"\n        if not self.user_id:\n            self.log(\"No user_id on component; skipping ingestion-run tracking.\")\n            return None, None, None, None\n\n        try:\n            user_uuid = uuid.UUID(str(self.user_id))\n        except (ValueError, TypeError) as exc:\n            self.log(f\"Could not coerce user_id={self.user_id!r} to UUID; skipping run tracking ({exc}).\")\n            return None, None, None, None\n\n        try:\n            from langflow.api.utils import ingestion_run_service, knowledge_base_service\n            from langflow.services.database.models.jobs.model import JobStatus, JobType\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion will not be recorded ({exc}).\")\n            return None, None, None, None\n\n        job_id: uuid.UUID | None = None\n        run_id: uuid.UUID | None = None\n        summary: IngestionSummary | None = None\n        try:\n            kb_record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n            kb_record_id = kb_record.id if kb_record is not None else None\n\n            user_metadata = self._parse_user_metadata_dict()\n\n            job_id = uuid.uuid4()\n            job_service = get_job_service()\n            raw_flow_id = getattr(self, \"flow_id\", None)\n            flow_id_uuid = uuid.UUID(str(raw_flow_id)) if raw_flow_id else job_id\n            source = FlowComponentSource(\n                user_id=user_uuid,\n                source_config={\n                    \"knowledge_base\": self.knowledge_base,\n                    \"kb_path\": str(kb_path),\n                    \"flow_id\": str(flow_id_uuid),\n                },\n            )\n            summary = IngestionSummary(\n                kb_name=self.knowledge_base,\n                source_type=source.source_type.value,\n                user_id=user_uuid,\n                job_id=job_id,\n                source_config=source.describe().get(\"config\") or {},\n                user_metadata=user_metadata,\n            )\n            # Inserted running and already heartbeated, so an orphan sweep in\n            # another worker never sees this live run without a heartbeat.\n            await job_service.create_job(\n                job_id=job_id,\n                flow_id=flow_id_uuid,\n                job_type=JobType.INGESTION,\n                asset_id=kb_record_id,\n                asset_type=\"knowledge_base\",\n                user_id=user_uuid,\n                status=JobStatus.IN_PROGRESS,\n                heartbeat=True,\n            )\n\n            run_id = await ingestion_run_service.create_run(\n                kb_name=self.knowledge_base,\n                source=source,\n                job_id=job_id,\n                user_id=user_uuid,\n                kb_id=kb_record_id,\n                user_metadata=user_metadata,\n            )\n            if run_id is None:\n                return None, job_id, None, kb_record_id\n            await ingestion_run_service.mark_running(run_id)\n\n            self.log(f\"Started ingestion run job_id={job_id} kb_name={self.knowledge_base} kb_id={kb_record_id}\")\n            # Keeps the job heartbeated until _finalize_ingestion_run stops it.\n            await job_service.start_keep_alive(job_id)\n        except asyncio.CancelledError:\n            # The caller has not received the run ID yet, so it cannot finalize\n            # a cancellation that arrives while tracking is being initialized.\n            if job_id is not None and summary is not None:\n                await _finish_despite_cancellation(\n                    self._finalize_ingestion_run(\n                        run_id=run_id or job_id,\n                        job_id=job_id,\n                        summary=summary,\n                        status=IngestionRunStatus.CANCELLED,\n                        error_message=\"Flow ingestion was cancelled.\",\n                    )\n                )\n            raise\n        except Exception as exc:  # noqa: BLE001 — telemetry must never abort ingestion\n            self.log(f\"Could not begin ingestion-run tracking: {exc}\")\n            return None, None, None, None\n        else:\n            return run_id, job_id, summary, kb_record_id\n\n    async def _finalize_ingestion_run(\n        self,\n        *,\n        run_id: uuid.UUID,\n        job_id: uuid.UUID | None,\n        summary: IngestionSummary,\n        status: IngestionRunStatus,\n        error_message: str | None,\n    ) -> None:\n        \"\"\"Persist the final summary and transition the parent Job.\"\"\"\n        try:\n            from langflow.api.utils import ingestion_run_service\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion-run finalize skipped ({exc}).\")\n            return\n\n        try:\n            if job_id is not None:\n                # The heartbeat also rewrites job_metadata, so it stops first.\n                await get_job_service().stop_keep_alive(job_id)\n            await ingestion_run_service.finalize_run(\n                run_id,\n                summary=summary,\n                status=status,\n                error_message=error_message,\n            )\n            if job_id is not None:\n                terminal_status = {\n                    IngestionRunStatus.FAILED: JobStatus.FAILED,\n                    IngestionRunStatus.CANCELLED: JobStatus.CANCELLED,\n                }.get(status, JobStatus.COMPLETED)\n                await get_job_service().update_job_status(job_id, terminal_status, finished_timestamp=True)\n        except Exception as exc:  # noqa: BLE001 — telemetry must never re-raise\n            self.log(f\"Could not finalize ingestion-run tracking: {exc}\")\n\n    async def _record_kb_status(\n        self,\n        kb_record_id: uuid.UUID,\n        status_value: str,\n        *,\n        failure_reason: str | None = None,\n    ) -> None:\n        \"\"\"Mirror Path A's KB-row status transitions.\"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n            from langflow.services.database.models.knowledge_base.model import KnowledgeBaseStatus\n        except ImportError:\n            return\n        try:\n            await knowledge_base_service.update_status(\n                kb_record_id,\n                status=KnowledgeBaseStatus(status_value),\n                failure_reason=failure_reason,\n            )\n        except Exception as exc:  # noqa: BLE001\n            self.log(f\"Could not update KB status to {status_value}: {exc}\")\n\n    def _parse_user_metadata_dict(self) -> dict[str, Any]:\n        \"\"\"Decode ``metadata_json`` to a dict, or ``{}`` on any error.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return {}\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return {}\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError):\n            return {}\n        return decoded if isinstance(decoded, dict) else {}\n\n    # =====================================================================\n    #                       RETRIEVAL CODE PATH\n    # =====================================================================\n    @property\n    def _user_uuid(self) -> uuid.UUID | None:\n        \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n        if not self.user_id:\n            return None\n        return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n    async def _get_kb_metadata(self, knowledge_base: str | None = None) -> dict:\n        \"\"\"Load this knowledge base's embedding config from its database row.\n\n        The row is the sole authority. There is no on-disk sidecar to fall back\n        to, which is what lets retrieval work identically on a replica whose\n        filesystem never held the KB directory.\n        \"\"\"\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return {}\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return {}\n        record = await knowledge_base_service.get_by_user_and_name(\n            user_uuid,\n            knowledge_base or self.knowledge_base,\n        )\n        if record is None:\n            return {}\n        return knowledge_base_service.record_to_metadata_dict(record)\n\n    async def _backend_from_record(self) -> tuple[str, dict[str, Any]] | None:\n        \"\"\"Read ``(backend_type, backend_config)`` off the KB's database row.\n\n        Returns ``None`` when there is no row to read — a legacy KB predating the\n        record, or bare lfx with no langflow installed. A lookup that *fails* is a\n        different situation and is allowed to propagate: guessing a backend after a\n        database error is how vectors end up in a store nothing queries.\n        \"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return None\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return None\n        record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n        if record is None:\n            return None\n        return record.backend_type or BackendType.CHROMA.value, record.backend_config or {}\n\n    async def _resolve_backend_config(self) -> tuple[str, dict[str, Any]]:\n        \"\"\"Resolve this KB's backend from its database row.\n\n        Ingestion and retrieval both land here, so they cannot disagree about\n        where this KB's vectors live. A backend that cannot be resolved raises\n        rather than defaulting to local storage: guessing is how a pod ends up\n        writing vectors to a local directory while queries follow the real config\n        to a remote store and silently find nothing.\n        \"\"\"\n        resolved = await self._backend_from_record()\n        if resolved is None:\n            msg = (\n                f\"Cannot determine the vector-store backend for knowledge base \"\n                f\"'{self.knowledge_base}': it has no database record. Refusing to fall \"\n                f\"back to local storage, which would write to a different store than \"\n                f\"queries read from. If this knowledge base predates database-backed \"\n                f\"metadata, adopt it with 'langflow reconcile-kb-from-disk'.\"\n            )\n            raise ValueError(msg)\n        return resolved\n\n    def _resolve_model_selection(self, metadata: dict[str, Any]) -> list[dict[str, Any]]:\n        \"\"\"Resolve the ``get_embeddings``-compatible model selection from metadata.\"\"\"\n        model_selection = metadata.get(\"model_selection\")\n        if model_selection:\n            selection_list = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            return [self._hydrate_model_metadata(entry) for entry in selection_list]\n\n        embedding_model_name = metadata.get(\"embedding_model\")\n        embedding_provider = metadata.get(\"embedding_provider\", \"Unknown\")\n        if not embedding_model_name:\n            msg = (\n                f\"Knowledge base '{self.knowledge_base}' has no embedding model recorded; \"\n                \"re-create it with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n\n        match = self._find_catalog_entry(embedding_model_name)\n        if match is None:\n            msg = (\n                f\"Embedding model '{embedding_model_name}' (provider '{embedding_provider}') \"\n                \"recorded for this knowledge base is no longer available in the model registry. \"\n                \"Please re-create the knowledge base with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n        return [match]\n\n    def _hydrate_model_metadata(self, entry: dict[str, Any]) -> dict[str, Any]:\n        \"\"\"Fill in ``metadata.embedding_class`` / ``param_mapping`` if missing.\"\"\"\n        entry_metadata = entry.get(\"metadata\") or {}\n        has_class = bool(entry_metadata.get(\"embedding_class\"))\n        has_mapping = bool(entry_metadata.get(\"param_mapping\"))\n        if has_class and has_mapping:\n            return entry\n\n        model_name = entry.get(\"name\")\n        if not model_name:\n            return entry\n\n        catalog_entry = self._find_catalog_entry(model_name)\n        if catalog_entry is None:\n            return entry\n\n        catalog_metadata = catalog_entry.get(\"metadata\") or {}\n        merged_metadata = {**catalog_metadata, **entry_metadata}\n        return {**entry, \"metadata\": merged_metadata}\n\n    def _find_catalog_entry(self, model_name: str) -> dict[str, Any] | None:\n        \"\"\"Look up an embedding model by name in the unified-models catalog.\"\"\"\n        options = get_embedding_model_options(user_id=self.user_id)\n        return next((o for o in options if o.get(\"name\") == model_name), None)\n\n    async def retrieve_data(self) -> DataFrame:\n        \"\"\"Retrieve data from the selected knowledge base.\n\n        Annotation narrowed to ``DataFrame`` to keep this output's handle\n        type list at ``[\"Table\"]`` only; widening to a union surfaces\n        ``[\"Table\", \"JSON\"]`` on the API and breaks every starter-project\n        edge whose sourceHandle was stored against a single-type handle\n        (BUG-02). The cross-mode defensive fallback may still return a\n        ``Data`` at runtime — Python ignores return annotations, so\n        nothing breaks.\n        \"\"\"\n        if not _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.build_kb_info()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        if not self.user_id:\n            msg = \"User ID is required for fetching Knowledge Base data.\"\n            raise ValueError(msg)\n\n        # Saved flows can load in standalone lfx, but knowledge retrieval\n        # requires the application's user database and storage services.\n        try:\n            from langflow.services.database.models.user.crud import get_user_by_id\n        except ModuleNotFoundError as exc:\n            if exc.name != \"langflow\":\n                raise\n            msg = \"Knowledge Base retrieval requires OpenXFlow's application services. Run this flow in OpenXFlow.\"\n            raise RuntimeError(msg) from None\n\n        async with session_scope() as db:\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n\n        metadata = await self._get_kb_metadata()\n        if not metadata:\n            msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n            raise ValueError(msg)\n\n        model_selection = self._resolve_model_selection(metadata)\n        chunk_size = metadata.get(\"chunk_size\")\n\n        # Resolve where this KB lives before instantiating embeddings: path\n        # containment is a cheap local check, and a request that will be refused\n        # shouldn't first pay for a credential lookup and a provider client.\n        backend_type, _backend_config = await self._resolve_backend_config()\n\n        # ``api_key=None`` is not \"no credential\": ``get_embeddings`` resolves the\n        # provider's key from the user's variables table and then the environment,\n        # exactly as the server-side ingestion path does. The component input just\n        # takes precedence when supplied.\n        embedding_function = get_embeddings(\n            model=model_selection,\n            user_id=self.user_id,\n            api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n            chunk_size=chunk_size,\n        )\n\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            user_metadata_filter = _parse_metadata_filter(getattr(self, \"metadata_filter\", None))\n            use_scores = bool(self.search_query)\n            exact_filter = backend_type == BackendType.SQLITE.value\n            search_k = self.top_k if exact_filter or not user_metadata_filter else self.top_k * 4\n            search_options = {\"source_filter\": user_metadata_filter} if exact_filter else {}\n            results = await backend.similarity_search(\n                query=self.search_query or \"\",\n                k=search_k,\n                with_scores=use_scores,\n                **search_options,\n            )\n            if user_metadata_filter and not exact_filter:\n                results = [\n                    (doc, score) for doc, score in results if _chunk_matches_filter(doc.metadata, user_metadata_filter)\n                ][: self.top_k]\n\n            embeddings_by_key: dict[tuple[str, str], list[float]] = {}\n            if self.include_embeddings and results:\n                # Join each retrieved chunk to its stored embedding. We key on\n                # ``_id`` when present and fall back to page content otherwise,\n                # so KBs populated by direct file upload — whose chunks carry no\n                # ``_id`` — still resolve. See ``_embedding_match_key``.\n                wanted_keys = {_embedding_match_key(doc.page_content, doc.metadata) for doc, _score in results}\n                async with aclosing(backend.iter_documents(include_embeddings=True)) as batches:\n                    async for batch in batches:\n                        for entry in batch:\n                            if entry.embedding is None:\n                                continue\n                            key = _embedding_match_key(entry.content, entry.metadata)\n                            if key in wanted_keys:\n                                embeddings_by_key[key] = entry.embedding\n                        if wanted_keys <= embeddings_by_key.keys():\n                            break\n\n            data_list: list[Data] = []\n            for doc, score in results:\n                kwargs: dict[str, Any] = {\"content\": doc.page_content}\n                if use_scores:\n                    kwargs[\"_score\"] = backend.normalize_score(score)\n                if self.include_metadata:\n                    kwargs.update(doc.metadata)\n                if self.include_embeddings:\n                    kwargs[\"_embeddings\"] = embeddings_by_key.get(_embedding_match_key(doc.page_content, doc.metadata))\n                data_list.append(Data(**kwargs))\n\n            return DataFrame(data=data_list)\n        finally:\n            await backend.teardown()\n\n\ndef _embedding_match_key(content: str, metadata: dict[str, Any] | None) -> tuple[str, str]:\n    \"\"\"Build a stable key for aligning a retrieved chunk with its stored embedding.\n\n    Embeddings are gathered separately (via ``iter_documents``) and then joined\n    back onto the search results. Component-driven ingestion stamps a\n    content-hash ``_id`` on every chunk, but direct file-upload ingestion\n    (``KBIngestionHelper.perform_ingestion``) does not. Keying the join purely on\n    ``_id`` therefore left every upload-populated KB with ``_embeddings: None``.\n\n    We prefer ``_id`` when present (so legitimately distinct chunks that happen to\n    share text aren't collapsed) and fall back to the chunk's page content\n    otherwise. The content fallback is exact for the embedding use case: two\n    chunks with identical text necessarily share the same embedding vector. The\n    leading ``\"id\"`` / ``\"content\"`` tag namespaces the two key spaces so a mixed\n    KB (some chunks with ``_id``, some without) never cross-matches.\n    \"\"\"\n    doc_id = metadata.get(\"_id\") if metadata else None\n    if doc_id:\n        return (\"id\", str(doc_id))\n    return (\"content\", content or \"\")\n\n\ndef _parse_metadata_filter(raw: str | None) -> dict[str, list[str]]:\n    \"\"\"Decode the ``metadata_filter`` input into a {key: [values]} map.\n\n    Empty or malformed input maps to an empty filter so retrieval falls back\n    to the unfiltered path. JSON errors are swallowed here rather than raised:\n    surfacing component-config errors at the canvas node would break a flow\n    run for what is meant to be an optional refinement.\n    \"\"\"\n    if not raw:\n        return {}\n    text = raw.strip() if isinstance(raw, str) else raw\n    if not text:\n        return {}\n    try:\n        decoded = json.loads(text)\n    except (TypeError, json.JSONDecodeError):\n        logger.warning(\"KnowledgeComponent: metadata_filter is not valid JSON; ignoring filter.\")\n        return {}\n    if not isinstance(decoded, dict):\n        logger.warning(\"KnowledgeComponent: metadata_filter must be a JSON object; ignoring filter.\")\n        return {}\n    result: dict[str, list[str]] = {}\n    for key, value in decoded.items():\n        if not isinstance(key, str):\n            continue\n        if isinstance(value, list):\n            result[key] = [str(entry) for entry in value]\n        else:\n            result[key] = [str(value)]\n    return result\n\n\ndef _chunk_matches_filter(metadata: dict[str, Any] | None, filt: dict[str, list[str]]) -> bool:\n    \"\"\"AND across keys, OR within key values, mirroring the chunks endpoint.\"\"\"\n    if not filt:\n        return True\n    if not metadata:\n        return False\n    raw = metadata.get(\"source_metadata\")\n    if not raw:\n        return False\n    try:\n        stored = json.loads(raw) if isinstance(raw, str) else raw\n    except json.JSONDecodeError:\n        return False\n    if not isinstance(stored, dict):\n        return False\n    for key, expected_values in filt.items():\n        actual = stored.get(key)\n        if actual is None:\n            return False\n        actual_set = {str(entry) for entry in actual} if isinstance(actual, list) else {str(actual)}\n        if not actual_set & set(expected_values):\n            return False\n    return True\n"
               },
               "column_config": {
                 "_input_type": "TableInput",
@@ -1355,11 +1355,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1371,13 +1372,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1527,7 +1527,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2098,7 +2098,7 @@
             "description": "# 📄 Document Q&A\n\nAsk questions about your documents and get answers grounded in their content,\nusing a Knowledge Base for retrieval.\n\n## Setup\n1. **Knowledge Base** — add your documents to a Knowledge Base first (via the\n   **Knowledge** module in OpenXFlow, or a separate ingestion flow). This flow\n   only **retrieves** — it does not ingest.\n2. **Knowledge (Retrieve)** — select the Knowledge Base that holds your documents.\n3. **Agent** — add your LLM key (any provider in the Model dropdown).\n\n## How it works\n1. **Chat Input → Knowledge (Retrieve)** — your question pulls the most relevant\n   passages from the Knowledge Base.\n2. **Parser → Prompt** — the retrieved passages become the context, combined with\n   your question.\n3. **Agent → Chat Output** — the Agent answers using **only** the retrieved\n   context (it won't make things up beyond your documents).\n\n## Run\nMake sure your Knowledge Base is populated, then type a question in the Playground.",
             "display_name": "",
             "documentation": "",
-            "i18n_key": "template_notes.document_q_a.0e21c05f",
+            "i18n_key": "template_notes.document_q_a.f7a494a0",
             "template": {
               "backgroundColor": "blue"
             }
@@ -2155,6 +2155,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -2163,7 +2164,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -2172,8 +2173,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -2445,4 +2445,4 @@
     "rag",
     "q-a"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json
index b762792ecd..a6035233c5 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json	
@@ -126,6 +126,7 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -134,7 +135,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -143,8 +144,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -622,7 +622,8 @@
             "legacy": false,
             "lf_version": "1.10.1rc3",
             "metadata": {
-              "code_hash": "6d53589f01c8",
+              "code_hash": "1bc59574381f",
+              "component_class_name": "FileComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -631,7 +632,7 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   },
                   {
                     "name": "pydantic",
@@ -644,8 +645,7 @@
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.files_and_knowledge.file.FileComponent",
-              "component_class_name": "FileComponent"
+              "module": "lfx.components.files_and_knowledge.file.FileComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -784,7 +784,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main OpenXFlow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = str(file.path)\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [str(f.path) for f in file_list]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
+                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = file.path.as_posix() if not file.path.is_absolute() else str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = (\n                    file.path.as_posix()\n                    if settings.storage_type == \"s3\" and not file.path.is_absolute()\n                    else str(file.path)\n                )\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [\n            f.path.as_posix() if settings.storage_type == \"s3\" and not f.path.is_absolute() else str(f.path)\n            for f in file_list\n        ]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
               },
               "concurrency_multithreading": {
                 "_input_type": "IntInput",
@@ -1336,11 +1336,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1352,13 +1353,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1508,7 +1508,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2202,4 +2202,4 @@
     "chatbots",
     "content-generation"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json
index 349afd4704..2f6e7dad21 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json	
@@ -261,7 +261,8 @@
             "legacy": false,
             "lf_version": "1.10.1rc3",
             "metadata": {
-              "code_hash": "6d53589f01c8",
+              "code_hash": "1bc59574381f",
+              "component_class_name": "FileComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -270,7 +271,7 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   },
                   {
                     "name": "pydantic",
@@ -283,8 +284,7 @@
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.files_and_knowledge.file.FileComponent",
-              "component_class_name": "FileComponent"
+              "module": "lfx.components.files_and_knowledge.file.FileComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -423,7 +423,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main OpenXFlow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = str(file.path)\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [str(f.path) for f in file_list]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
+                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = file.path.as_posix() if not file.path.is_absolute() else str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = (\n                    file.path.as_posix()\n                    if settings.storage_type == \"s3\" and not file.path.is_absolute()\n                    else str(file.path)\n                )\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [\n            f.path.as_posix() if settings.storage_type == \"s3\" and not f.path.is_absolute() else str(f.path)\n            for f in file_list\n        ]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
               },
               "concurrency_multithreading": {
                 "_input_type": "IntInput",
@@ -1454,7 +1454,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from astrapy import DataAPIClient\nfrom langchain_core.documents import Document\n\nfrom lfx_datastax.base.astradb_base import AstraDBBaseComponent\nfrom lfx.base.models.unified_models import (\n    apply_provider_variable_config_to_build_config,\n    get_embedding_model_options,\n    get_embeddings,\n    update_model_options_in_build_config,\n)\nfrom lfx.base.vectorstores.model import (\n    LCVectorStoreComponent,\n    check_cached_vector_store,\n)\nfrom lfx.base.vectorstores.vector_store_connection_decorator import (\n    vector_store_connection,\n)\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import (\n    BoolInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    ModelInput,\n    NestedDictInput,\n    QueryInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.serialization import serialize\nfrom lfx.utils.version import get_version_info\n\n\n@vector_store_connection\nclass AstraDBVectorStoreComponent(AstraDBBaseComponent, LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.langflow.org/bundles-datastax\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        *AstraDBBaseComponent.inputs,\n        *LCVectorStoreComponent.inputs,\n        ModelInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            info=\"Specify the Embedding Model. Not required for Astra Vectorize collections.\",\n            required=False,\n            show=True,\n            model_type=\"embedding\",\n            input_types=[\"Embeddings\"],\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"search_method\",\n            display_name=\"Search Method\",\n            info=(\n                \"Determine how your content is matched: Vector finds semantic similarity, \"\n                \"and Hybrid Search (suggested) combines both approaches \"\n                \"with a reranker.\"\n            ),\n            options=[\"Hybrid Search\", \"Vector Search\"],  # TODO: Restore Lexical Search?\n            options_metadata=[{\"icon\": \"SearchHybrid\"}, {\"icon\": \"SearchVector\"}],\n            value=\"Vector Search\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"reranker\",\n            display_name=\"Reranker\",\n            info=\"Post-retrieval model that re-scores results for optimal relevance ranking.\",\n            show=False,\n            toggle=True,\n        ),\n        QueryInput(\n            name=\"lexical_terms\",\n            display_name=\"Lexical Terms\",\n            info=\"Add additional terms/keywords to augment search precision.\",\n            placeholder=\"Enter terms to search...\",\n            separator=\" \",\n            show=False,\n            value=\"\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\n                \"Similarity\",\n                \"Similarity with score threshold\",\n                \"MMR (Max Marginal Relevance)\",\n            ],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n    ]\n\n    async def update_build_config(\n        self,\n        build_config: dict,\n        field_value: str | dict,\n        field_name: str | None = None,\n    ) -> dict:\n        \"\"\"Update build configuration with proper handling of embedding and search options.\"\"\"\n        # Update model options for ModelInput only when relevant.\n        # Only refresh when: initial load (None), embedding_model field changes, or api_key changes.\n        #\n        # Note: this intentionally uses update_model_options_in_build_config directly rather than\n        # handle_model_input_update because:\n        #   1. The model field is named \"embedding_model\" (not \"model\"), requiring model_field_name.\n        #   2. The refresh is conditional - we skip it for unrelated fields (e.g. database_name,\n        #      collection_name) to avoid unnecessary work.\n        #   3. AstraDB manages its own provider-field visibility (embedding_generation_provider\n        #      dialog), so the generic provider-field hide/show steps in handle_model_input_update\n        #      are not applicable here.\n        if field_name in (None, \"embedding_model\", \"api_key\"):\n            build_config = update_model_options_in_build_config(\n                component=self,\n                build_config=build_config,\n                cache_key_prefix=\"embedding_model_options\",\n                get_options_func=get_embedding_model_options,\n                field_name=field_name,\n                field_value=field_value if field_name == \"embedding_model\" else None,\n                model_field_name=\"embedding_model\",\n            )\n\n            # Auto-populate API key based on the selected embedding model's provider.\n            # Skip when user directly edits api_key to preserve their value.\n            if field_name != \"api_key\":\n                model_value = build_config.get(\"embedding_model\", {}).get(\"value\")\n                if isinstance(model_value, list) and model_value:\n                    provider = model_value[0].get(\"provider\", \"\")\n                    if provider:\n                        build_config = apply_provider_variable_config_to_build_config(build_config, provider)\n\n            # Ensure the API key field is always visible\n            if \"api_key\" in build_config:\n                build_config[\"api_key\"][\"show\"] = True\n\n        # Handle base astra db build config updates\n        build_config = await super().update_build_config(\n            build_config,\n            field_value=field_value,\n            field_name=field_name,\n        )\n\n        # Set embedding model display based on provider selection\n        if isinstance(field_value, dict) and \"02_embedding_generation_provider\" in field_value:\n            embedding_provider = field_value.get(\"02_embedding_generation_provider\")\n            is_custom_provider = embedding_provider and embedding_provider != \"Bring your own\"\n            provider = embedding_provider.lower() if is_custom_provider and embedding_provider is not None else None\n\n            build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n            build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Early return if no API endpoint is configured\n        if not self.get_api_endpoint():\n            return build_config\n\n        # Configure search method and related options\n        return self._configure_search_options(build_config)\n\n    def _configure_search_options(self, build_config: dict) -> dict:\n        \"\"\"Configure hybrid search, reranker, and vector search options.\"\"\"\n        # Detect available hybrid search capabilities\n        hybrid_capabilities = self._detect_hybrid_capabilities()\n\n        # Return if we haven't selected a collection\n        if not build_config[\"collection_name\"][\"options\"] or not build_config[\"collection_name\"][\"value\"]:\n            return build_config\n\n        # Get collection options\n        collection_options = self._get_collection_options(build_config)\n\n        # Get the selected collection index\n        index = build_config[\"collection_name\"][\"options\"].index(build_config[\"collection_name\"][\"value\"])\n        provider = build_config[\"collection_name\"][\"options_metadata\"][index][\"provider\"]\n        build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n        build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Determine search configuration\n        is_vector_search = build_config[\"search_method\"][\"value\"] == \"Vector Search\"\n        is_autodetect = build_config[\"autodetect_collection\"][\"value\"]\n\n        # Apply hybrid search configuration\n        if hybrid_capabilities[\"available\"]:\n            build_config[\"search_method\"][\"show\"] = True\n            build_config[\"search_method\"][\"options\"] = [\n                \"Hybrid Search\",\n                \"Vector Search\",\n            ]\n            build_config[\"search_method\"][\"value\"] = build_config[\"search_method\"].get(\"value\", \"Hybrid Search\")\n\n            build_config[\"reranker\"][\"options\"] = hybrid_capabilities[\"reranker_models\"]\n            build_config[\"reranker\"][\"options_metadata\"] = hybrid_capabilities[\"reranker_metadata\"]\n            if hybrid_capabilities[\"reranker_models\"]:\n                build_config[\"reranker\"][\"value\"] = hybrid_capabilities[\"reranker_models\"][0]\n        else:\n            build_config[\"search_method\"][\"show\"] = False\n            build_config[\"search_method\"][\"options\"] = [\"Vector Search\"]\n            build_config[\"search_method\"][\"value\"] = \"Vector Search\"\n            build_config[\"reranker\"][\"options\"] = []\n            build_config[\"reranker\"][\"options_metadata\"] = []\n\n        # Configure reranker visibility and state\n        hybrid_enabled = (\n            collection_options[\"rerank_enabled\"] and build_config[\"search_method\"][\"value\"] == \"Hybrid Search\"\n        )\n\n        build_config[\"reranker\"][\"show\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_value\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_disable\"] = is_vector_search\n\n        # Configure lexical terms\n        lexical_visible = collection_options[\"lexical_enabled\"] and not is_vector_search\n        build_config[\"lexical_terms\"][\"show\"] = lexical_visible\n        build_config[\"lexical_terms\"][\"value\"] = \"\" if is_vector_search else build_config[\"lexical_terms\"][\"value\"]\n\n        # Configure search type and score threshold\n        build_config[\"search_type\"][\"show\"] = is_vector_search\n        build_config[\"search_score_threshold\"][\"show\"] = is_vector_search\n\n        # Force similarity search for hybrid mode or autodetect\n        if hybrid_enabled or is_autodetect:\n            build_config[\"search_type\"][\"value\"] = \"Similarity\"\n\n        return build_config\n\n    def _detect_hybrid_capabilities(self) -> dict:\n        \"\"\"Detect available hybrid search and reranking capabilities.\"\"\"\n        environment = self.get_environment(self.environment)\n        client = DataAPIClient(environment=environment)\n        admin_client = client.get_admin()\n        db_admin = admin_client.get_database_admin(self.get_api_endpoint(), token=self.token)\n\n        try:\n            providers = db_admin.find_reranking_providers()\n            reranker_models = [\n                model.name for provider_data in providers.reranking_providers.values() for model in provider_data.models\n            ]\n            reranker_metadata = [\n                {\"icon\": self.get_provider_icon(provider_name=model.name.split(\"/\")[0])}\n                for provider in providers.reranking_providers.values()\n                for model in provider.models\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Hybrid search not available: {e}\")\n            return {\n                \"available\": False,\n                \"reranker_models\": [],\n                \"reranker_metadata\": [],\n            }\n        else:\n            return {\n                \"available\": True,\n                \"reranker_models\": reranker_models,\n                \"reranker_metadata\": reranker_metadata,\n            }\n\n    def _get_collection_options(self, build_config: dict) -> dict:\n        \"\"\"Retrieve collection-level search options.\"\"\"\n        database = self.get_database_object(api_endpoint=build_config[\"api_endpoint\"][\"value\"])\n        collection = database.get_collection(\n            name=build_config[\"collection_name\"][\"value\"],\n            keyspace=build_config[\"keyspace\"][\"value\"],\n        )\n\n        col_options = collection.options()\n\n        return {\n            \"rerank_enabled\": bool(col_options.rerank and col_options.rerank.enabled),\n            \"lexical_enabled\": bool(col_options.lexical and col_options.lexical.enabled),\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import HybridSearchMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Resolve the embedding model from ModelInput selection to an Embeddings instance\n        embedding = None\n        if self.embedding_model:\n            embedding = get_embeddings(\n                model=self.embedding_model,\n                user_id=self.user_id,\n                api_key=getattr(self, \"api_key\", None),\n            )\n        embedding_params = {\"embedding\": embedding} if embedding else {}\n\n        # Get the additional parameters\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get OpenXFlow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        # if os.getenv(\"AWS_EXECUTION_ENV\") == \"AWS_ECS_FARGATE\":  # TODO: More precise way of detecting\n        #     langflow_prefix = \"ds-\"\n\n        # Get the database object\n        database = self.get_database_object()\n        autodetect = self.collection_name in database.list_collection_names() and self.autodetect_collection\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": autodetect,\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params\n                    and self.collection_data(collection_name=self.collection_name, database=database) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Choose HybridSearchMode based on the selected param\n        hybrid_search_mode = HybridSearchMode.DEFAULT if self.search_method == \"Hybrid Search\" else HybridSearchMode.OFF\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=database.api_endpoint,\n                namespace=database.keyspace,\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Hybrid Search Parameters\n                hybrid_search=hybrid_search_mode,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except ValueError as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        # Add documents to the vector store\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        self.ingest_data = self._prepare_ingest_data()\n\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        documents = [\n            Document(\n                page_content=doc.page_content,\n                metadata=serialize(doc.metadata, to_str=True),\n            )\n            for doc in documents\n        ]\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=database.keyspace)\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except ValueError as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except ValueError as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        # Clean up the search query\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n        lexical_terms = self.lexical_terms or None\n\n        # Check if we have a search query, and if so set the args\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n                \"lexical_query\": lexical_terms,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n        self.log(f\"store.hybrid_search: {vector_store.hybrid_search}\")\n        self.log(f\"Lexical terms: {self.lexical_terms}\")\n        self.log(f\"Reranker: {self.reranker}\")\n\n        try:\n            search_args = self._build_search_args()\n        except ValueError as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except ValueError as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+                "value": "from astrapy import DataAPIClient\nfrom langchain_core.documents import Document\n\nfrom lfx_datastax.base.astradb_base import AstraDBBaseComponent\nfrom lfx.base.models.unified_models import (\n    apply_provider_variable_config_to_build_config,\n    get_embedding_model_options,\n    get_embeddings,\n    update_model_options_in_build_config,\n)\nfrom lfx.base.vectorstores.model import (\n    LCVectorStoreComponent,\n    check_cached_vector_store,\n)\nfrom lfx.base.vectorstores.vector_store_connection_decorator import (\n    vector_store_connection,\n)\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import (\n    BoolInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    ModelInput,\n    NestedDictInput,\n    QueryInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.serialization import serialize\nfrom lfx.utils.version import get_version_info\n\n\n@vector_store_connection\nclass AstraDBVectorStoreComponent(AstraDBBaseComponent, LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.langflow.org/bundles-datastax\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        *AstraDBBaseComponent.inputs,\n        *LCVectorStoreComponent.inputs,\n        ModelInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            info=\"Specify the Embedding Model. Not required for Astra Vectorize collections.\",\n            required=False,\n            show=True,\n            model_type=\"embedding\",\n            input_types=[\"Embeddings\"],\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"search_method\",\n            display_name=\"Search Method\",\n            info=(\n                \"Determine how your content is matched: Vector finds semantic similarity, \"\n                \"and Hybrid Search (suggested) combines both approaches \"\n                \"with a reranker.\"\n            ),\n            options=[\"Hybrid Search\", \"Vector Search\"],  # TODO: Restore Lexical Search?\n            options_metadata=[{\"icon\": \"SearchHybrid\"}, {\"icon\": \"SearchVector\"}],\n            value=\"Vector Search\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"reranker\",\n            display_name=\"Reranker\",\n            info=\"Post-retrieval model that re-scores results for optimal relevance ranking.\",\n            show=False,\n            toggle=True,\n        ),\n        QueryInput(\n            name=\"lexical_terms\",\n            display_name=\"Lexical Terms\",\n            info=\"Add additional terms/keywords to augment search precision.\",\n            placeholder=\"Enter terms to search...\",\n            separator=\" \",\n            show=False,\n            value=\"\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\n                \"Similarity\",\n                \"Similarity with score threshold\",\n                \"MMR (Max Marginal Relevance)\",\n            ],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n    ]\n\n    async def update_build_config(\n        self,\n        build_config: dict,\n        field_value: str | dict,\n        field_name: str | None = None,\n    ) -> dict:\n        \"\"\"Update build configuration with proper handling of embedding and search options.\"\"\"\n        # Update model options for ModelInput only when relevant.\n        # Only refresh when: initial load (None), embedding_model field changes, or api_key changes.\n        #\n        # Note: this intentionally uses update_model_options_in_build_config directly rather than\n        # handle_model_input_update because:\n        #   1. The model field is named \"embedding_model\" (not \"model\"), requiring model_field_name.\n        #   2. The refresh is conditional - we skip it for unrelated fields (e.g. database_name,\n        #      collection_name) to avoid unnecessary work.\n        #   3. AstraDB manages its own provider-field visibility (embedding_generation_provider\n        #      dialog), so the generic provider-field hide/show steps in handle_model_input_update\n        #      are not applicable here.\n        if field_name in (None, \"embedding_model\", \"api_key\"):\n            build_config = update_model_options_in_build_config(\n                component=self,\n                build_config=build_config,\n                cache_key_prefix=\"embedding_model_options\",\n                get_options_func=get_embedding_model_options,\n                field_name=field_name,\n                field_value=field_value if field_name == \"embedding_model\" else None,\n                model_field_name=\"embedding_model\",\n            )\n\n            # Auto-populate API key based on the selected embedding model's provider.\n            # Skip when user directly edits api_key to preserve their value.\n            if field_name != \"api_key\":\n                model_value = build_config.get(\"embedding_model\", {}).get(\"value\")\n                if isinstance(model_value, list) and model_value:\n                    provider = model_value[0].get(\"provider\", \"\")\n                    if provider:\n                        build_config = apply_provider_variable_config_to_build_config(build_config, provider)\n\n            # Ensure the API key field is always visible\n            if \"api_key\" in build_config:\n                build_config[\"api_key\"][\"show\"] = True\n\n        # Handle base astra db build config updates\n        build_config = await super().update_build_config(\n            build_config,\n            field_value=field_value,\n            field_name=field_name,\n        )\n\n        # Set embedding model display based on provider selection\n        if isinstance(field_value, dict) and \"02_embedding_generation_provider\" in field_value:\n            embedding_provider = field_value.get(\"02_embedding_generation_provider\")\n            is_custom_provider = embedding_provider and embedding_provider != \"Bring your own\"\n            provider = embedding_provider.lower() if is_custom_provider and embedding_provider is not None else None\n\n            build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n            build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Early return if no API endpoint is configured\n        if not self.get_api_endpoint():\n            return build_config\n\n        # Configure search method and related options\n        return self._configure_search_options(build_config)\n\n    def _configure_search_options(self, build_config: dict) -> dict:\n        \"\"\"Configure hybrid search, reranker, and vector search options.\"\"\"\n        # Detect available hybrid search capabilities\n        hybrid_capabilities = self._detect_hybrid_capabilities()\n\n        # Return if we haven't selected a collection\n        if not build_config[\"collection_name\"][\"options\"] or not build_config[\"collection_name\"][\"value\"]:\n            return build_config\n\n        # Get collection options\n        collection_options = self._get_collection_options(build_config)\n\n        # Get the selected collection index\n        index = build_config[\"collection_name\"][\"options\"].index(build_config[\"collection_name\"][\"value\"])\n        provider = build_config[\"collection_name\"][\"options_metadata\"][index][\"provider\"]\n        build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n        build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Determine search configuration\n        is_vector_search = build_config[\"search_method\"][\"value\"] == \"Vector Search\"\n        is_autodetect = build_config[\"autodetect_collection\"][\"value\"]\n\n        # Apply hybrid search configuration\n        if hybrid_capabilities[\"available\"]:\n            build_config[\"search_method\"][\"show\"] = True\n            build_config[\"search_method\"][\"options\"] = [\n                \"Hybrid Search\",\n                \"Vector Search\",\n            ]\n            build_config[\"search_method\"][\"value\"] = build_config[\"search_method\"].get(\"value\", \"Hybrid Search\")\n\n            build_config[\"reranker\"][\"options\"] = hybrid_capabilities[\"reranker_models\"]\n            build_config[\"reranker\"][\"options_metadata\"] = hybrid_capabilities[\"reranker_metadata\"]\n            if hybrid_capabilities[\"reranker_models\"]:\n                build_config[\"reranker\"][\"value\"] = hybrid_capabilities[\"reranker_models\"][0]\n        else:\n            build_config[\"search_method\"][\"show\"] = False\n            build_config[\"search_method\"][\"options\"] = [\"Vector Search\"]\n            build_config[\"search_method\"][\"value\"] = \"Vector Search\"\n            build_config[\"reranker\"][\"options\"] = []\n            build_config[\"reranker\"][\"options_metadata\"] = []\n\n        # Configure reranker visibility and state\n        hybrid_enabled = (\n            collection_options[\"rerank_enabled\"] and build_config[\"search_method\"][\"value\"] == \"Hybrid Search\"\n        )\n\n        build_config[\"reranker\"][\"show\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_value\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_disable\"] = is_vector_search\n\n        # Configure lexical terms\n        lexical_visible = collection_options[\"lexical_enabled\"] and not is_vector_search\n        build_config[\"lexical_terms\"][\"show\"] = lexical_visible\n        build_config[\"lexical_terms\"][\"value\"] = \"\" if is_vector_search else build_config[\"lexical_terms\"][\"value\"]\n\n        # Configure search type and score threshold\n        build_config[\"search_type\"][\"show\"] = is_vector_search\n        build_config[\"search_score_threshold\"][\"show\"] = is_vector_search\n\n        # Force similarity search for hybrid mode or autodetect\n        if hybrid_enabled or is_autodetect:\n            build_config[\"search_type\"][\"value\"] = \"Similarity\"\n\n        return build_config\n\n    def _detect_hybrid_capabilities(self) -> dict:\n        \"\"\"Detect available hybrid search and reranking capabilities.\"\"\"\n        environment = self.get_environment(self.environment)\n        client = DataAPIClient(environment=environment)\n        admin_client = client.get_admin()\n        db_admin = admin_client.get_database_admin(self.get_api_endpoint(), token=self.token)\n\n        try:\n            providers = db_admin.find_reranking_providers()\n            reranker_models = [\n                model.name for provider_data in providers.reranking_providers.values() for model in provider_data.models\n            ]\n            reranker_metadata = [\n                {\"icon\": self.get_provider_icon(provider_name=model.name.split(\"/\")[0])}\n                for provider in providers.reranking_providers.values()\n                for model in provider.models\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Hybrid search not available: {e}\")\n            return {\n                \"available\": False,\n                \"reranker_models\": [],\n                \"reranker_metadata\": [],\n            }\n        else:\n            return {\n                \"available\": True,\n                \"reranker_models\": reranker_models,\n                \"reranker_metadata\": reranker_metadata,\n            }\n\n    def _get_collection_options(self, build_config: dict) -> dict:\n        \"\"\"Retrieve collection-level search options.\"\"\"\n        database = self.get_database_object(api_endpoint=build_config[\"api_endpoint\"][\"value\"])\n        collection = database.get_collection(\n            name=build_config[\"collection_name\"][\"value\"],\n            keyspace=build_config[\"keyspace\"][\"value\"],\n        )\n\n        col_options = collection.options()\n\n        return {\n            \"rerank_enabled\": bool(col_options.rerank and col_options.rerank.enabled),\n            \"lexical_enabled\": bool(col_options.lexical and col_options.lexical.enabled),\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import HybridSearchMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Resolve the embedding model from ModelInput selection to an Embeddings instance\n        embedding = None\n        if self.embedding_model:\n            embedding = get_embeddings(\n                model=self.embedding_model,\n                user_id=self.user_id,\n                api_key=getattr(self, \"api_key\", None),\n            )\n        embedding_params = {\"embedding\": embedding} if embedding else {}\n\n        # Get the additional parameters\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        # if os.getenv(\"AWS_EXECUTION_ENV\") == \"AWS_ECS_FARGATE\":  # TODO: More precise way of detecting\n        #     langflow_prefix = \"ds-\"\n\n        # Get the database object\n        database = self.get_database_object()\n        autodetect = self.collection_name in database.list_collection_names() and self.autodetect_collection\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": autodetect,\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params\n                    and self.collection_data(collection_name=self.collection_name, database=database) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Choose HybridSearchMode based on the selected param\n        hybrid_search_mode = HybridSearchMode.DEFAULT if self.search_method == \"Hybrid Search\" else HybridSearchMode.OFF\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=database.api_endpoint,\n                namespace=database.keyspace,\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Hybrid Search Parameters\n                hybrid_search=hybrid_search_mode,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except ValueError as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        # Add documents to the vector store\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        self.ingest_data = self._prepare_ingest_data()\n\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        documents = [\n            Document(\n                page_content=doc.page_content,\n                metadata=serialize(doc.metadata, to_str=True),\n            )\n            for doc in documents\n        ]\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=database.keyspace)\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except ValueError as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except ValueError as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        # Clean up the search query\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n        lexical_terms = self.lexical_terms or None\n\n        # Check if we have a search query, and if so set the args\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n                \"lexical_query\": lexical_terms,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n        self.log(f\"store.hybrid_search: {vector_store.hybrid_search}\")\n        self.log(f\"Lexical terms: {self.lexical_terms}\")\n        self.log(f\"Reranker: {self.reranker}\")\n\n        try:\n            search_args = self._build_search_args()\n        except ValueError as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except ValueError as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
               },
               "collection_name": {
                 "_input_type": "DropdownInput",
@@ -2514,11 +2514,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2530,13 +2531,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2686,7 +2686,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -3500,7 +3500,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from astrapy import DataAPIClient\nfrom langchain_core.documents import Document\n\nfrom lfx_datastax.base.astradb_base import AstraDBBaseComponent\nfrom lfx.base.models.unified_models import (\n    apply_provider_variable_config_to_build_config,\n    get_embedding_model_options,\n    get_embeddings,\n    update_model_options_in_build_config,\n)\nfrom lfx.base.vectorstores.model import (\n    LCVectorStoreComponent,\n    check_cached_vector_store,\n)\nfrom lfx.base.vectorstores.vector_store_connection_decorator import (\n    vector_store_connection,\n)\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import (\n    BoolInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    ModelInput,\n    NestedDictInput,\n    QueryInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.serialization import serialize\nfrom lfx.utils.version import get_version_info\n\n\n@vector_store_connection\nclass AstraDBVectorStoreComponent(AstraDBBaseComponent, LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.langflow.org/bundles-datastax\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        *AstraDBBaseComponent.inputs,\n        *LCVectorStoreComponent.inputs,\n        ModelInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            info=\"Specify the Embedding Model. Not required for Astra Vectorize collections.\",\n            required=False,\n            show=True,\n            model_type=\"embedding\",\n            input_types=[\"Embeddings\"],\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"search_method\",\n            display_name=\"Search Method\",\n            info=(\n                \"Determine how your content is matched: Vector finds semantic similarity, \"\n                \"and Hybrid Search (suggested) combines both approaches \"\n                \"with a reranker.\"\n            ),\n            options=[\"Hybrid Search\", \"Vector Search\"],  # TODO: Restore Lexical Search?\n            options_metadata=[{\"icon\": \"SearchHybrid\"}, {\"icon\": \"SearchVector\"}],\n            value=\"Vector Search\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"reranker\",\n            display_name=\"Reranker\",\n            info=\"Post-retrieval model that re-scores results for optimal relevance ranking.\",\n            show=False,\n            toggle=True,\n        ),\n        QueryInput(\n            name=\"lexical_terms\",\n            display_name=\"Lexical Terms\",\n            info=\"Add additional terms/keywords to augment search precision.\",\n            placeholder=\"Enter terms to search...\",\n            separator=\" \",\n            show=False,\n            value=\"\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\n                \"Similarity\",\n                \"Similarity with score threshold\",\n                \"MMR (Max Marginal Relevance)\",\n            ],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n    ]\n\n    async def update_build_config(\n        self,\n        build_config: dict,\n        field_value: str | dict,\n        field_name: str | None = None,\n    ) -> dict:\n        \"\"\"Update build configuration with proper handling of embedding and search options.\"\"\"\n        # Update model options for ModelInput only when relevant.\n        # Only refresh when: initial load (None), embedding_model field changes, or api_key changes.\n        #\n        # Note: this intentionally uses update_model_options_in_build_config directly rather than\n        # handle_model_input_update because:\n        #   1. The model field is named \"embedding_model\" (not \"model\"), requiring model_field_name.\n        #   2. The refresh is conditional - we skip it for unrelated fields (e.g. database_name,\n        #      collection_name) to avoid unnecessary work.\n        #   3. AstraDB manages its own provider-field visibility (embedding_generation_provider\n        #      dialog), so the generic provider-field hide/show steps in handle_model_input_update\n        #      are not applicable here.\n        if field_name in (None, \"embedding_model\", \"api_key\"):\n            build_config = update_model_options_in_build_config(\n                component=self,\n                build_config=build_config,\n                cache_key_prefix=\"embedding_model_options\",\n                get_options_func=get_embedding_model_options,\n                field_name=field_name,\n                field_value=field_value if field_name == \"embedding_model\" else None,\n                model_field_name=\"embedding_model\",\n            )\n\n            # Auto-populate API key based on the selected embedding model's provider.\n            # Skip when user directly edits api_key to preserve their value.\n            if field_name != \"api_key\":\n                model_value = build_config.get(\"embedding_model\", {}).get(\"value\")\n                if isinstance(model_value, list) and model_value:\n                    provider = model_value[0].get(\"provider\", \"\")\n                    if provider:\n                        build_config = apply_provider_variable_config_to_build_config(build_config, provider)\n\n            # Ensure the API key field is always visible\n            if \"api_key\" in build_config:\n                build_config[\"api_key\"][\"show\"] = True\n\n        # Handle base astra db build config updates\n        build_config = await super().update_build_config(\n            build_config,\n            field_value=field_value,\n            field_name=field_name,\n        )\n\n        # Set embedding model display based on provider selection\n        if isinstance(field_value, dict) and \"02_embedding_generation_provider\" in field_value:\n            embedding_provider = field_value.get(\"02_embedding_generation_provider\")\n            is_custom_provider = embedding_provider and embedding_provider != \"Bring your own\"\n            provider = embedding_provider.lower() if is_custom_provider and embedding_provider is not None else None\n\n            build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n            build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Early return if no API endpoint is configured\n        if not self.get_api_endpoint():\n            return build_config\n\n        # Configure search method and related options\n        return self._configure_search_options(build_config)\n\n    def _configure_search_options(self, build_config: dict) -> dict:\n        \"\"\"Configure hybrid search, reranker, and vector search options.\"\"\"\n        # Detect available hybrid search capabilities\n        hybrid_capabilities = self._detect_hybrid_capabilities()\n\n        # Return if we haven't selected a collection\n        if not build_config[\"collection_name\"][\"options\"] or not build_config[\"collection_name\"][\"value\"]:\n            return build_config\n\n        # Get collection options\n        collection_options = self._get_collection_options(build_config)\n\n        # Get the selected collection index\n        index = build_config[\"collection_name\"][\"options\"].index(build_config[\"collection_name\"][\"value\"])\n        provider = build_config[\"collection_name\"][\"options_metadata\"][index][\"provider\"]\n        build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n        build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Determine search configuration\n        is_vector_search = build_config[\"search_method\"][\"value\"] == \"Vector Search\"\n        is_autodetect = build_config[\"autodetect_collection\"][\"value\"]\n\n        # Apply hybrid search configuration\n        if hybrid_capabilities[\"available\"]:\n            build_config[\"search_method\"][\"show\"] = True\n            build_config[\"search_method\"][\"options\"] = [\n                \"Hybrid Search\",\n                \"Vector Search\",\n            ]\n            build_config[\"search_method\"][\"value\"] = build_config[\"search_method\"].get(\"value\", \"Hybrid Search\")\n\n            build_config[\"reranker\"][\"options\"] = hybrid_capabilities[\"reranker_models\"]\n            build_config[\"reranker\"][\"options_metadata\"] = hybrid_capabilities[\"reranker_metadata\"]\n            if hybrid_capabilities[\"reranker_models\"]:\n                build_config[\"reranker\"][\"value\"] = hybrid_capabilities[\"reranker_models\"][0]\n        else:\n            build_config[\"search_method\"][\"show\"] = False\n            build_config[\"search_method\"][\"options\"] = [\"Vector Search\"]\n            build_config[\"search_method\"][\"value\"] = \"Vector Search\"\n            build_config[\"reranker\"][\"options\"] = []\n            build_config[\"reranker\"][\"options_metadata\"] = []\n\n        # Configure reranker visibility and state\n        hybrid_enabled = (\n            collection_options[\"rerank_enabled\"] and build_config[\"search_method\"][\"value\"] == \"Hybrid Search\"\n        )\n\n        build_config[\"reranker\"][\"show\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_value\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_disable\"] = is_vector_search\n\n        # Configure lexical terms\n        lexical_visible = collection_options[\"lexical_enabled\"] and not is_vector_search\n        build_config[\"lexical_terms\"][\"show\"] = lexical_visible\n        build_config[\"lexical_terms\"][\"value\"] = \"\" if is_vector_search else build_config[\"lexical_terms\"][\"value\"]\n\n        # Configure search type and score threshold\n        build_config[\"search_type\"][\"show\"] = is_vector_search\n        build_config[\"search_score_threshold\"][\"show\"] = is_vector_search\n\n        # Force similarity search for hybrid mode or autodetect\n        if hybrid_enabled or is_autodetect:\n            build_config[\"search_type\"][\"value\"] = \"Similarity\"\n\n        return build_config\n\n    def _detect_hybrid_capabilities(self) -> dict:\n        \"\"\"Detect available hybrid search and reranking capabilities.\"\"\"\n        environment = self.get_environment(self.environment)\n        client = DataAPIClient(environment=environment)\n        admin_client = client.get_admin()\n        db_admin = admin_client.get_database_admin(self.get_api_endpoint(), token=self.token)\n\n        try:\n            providers = db_admin.find_reranking_providers()\n            reranker_models = [\n                model.name for provider_data in providers.reranking_providers.values() for model in provider_data.models\n            ]\n            reranker_metadata = [\n                {\"icon\": self.get_provider_icon(provider_name=model.name.split(\"/\")[0])}\n                for provider in providers.reranking_providers.values()\n                for model in provider.models\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Hybrid search not available: {e}\")\n            return {\n                \"available\": False,\n                \"reranker_models\": [],\n                \"reranker_metadata\": [],\n            }\n        else:\n            return {\n                \"available\": True,\n                \"reranker_models\": reranker_models,\n                \"reranker_metadata\": reranker_metadata,\n            }\n\n    def _get_collection_options(self, build_config: dict) -> dict:\n        \"\"\"Retrieve collection-level search options.\"\"\"\n        database = self.get_database_object(api_endpoint=build_config[\"api_endpoint\"][\"value\"])\n        collection = database.get_collection(\n            name=build_config[\"collection_name\"][\"value\"],\n            keyspace=build_config[\"keyspace\"][\"value\"],\n        )\n\n        col_options = collection.options()\n\n        return {\n            \"rerank_enabled\": bool(col_options.rerank and col_options.rerank.enabled),\n            \"lexical_enabled\": bool(col_options.lexical and col_options.lexical.enabled),\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import HybridSearchMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Resolve the embedding model from ModelInput selection to an Embeddings instance\n        embedding = None\n        if self.embedding_model:\n            embedding = get_embeddings(\n                model=self.embedding_model,\n                user_id=self.user_id,\n                api_key=getattr(self, \"api_key\", None),\n            )\n        embedding_params = {\"embedding\": embedding} if embedding else {}\n\n        # Get the additional parameters\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get OpenXFlow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        # if os.getenv(\"AWS_EXECUTION_ENV\") == \"AWS_ECS_FARGATE\":  # TODO: More precise way of detecting\n        #     langflow_prefix = \"ds-\"\n\n        # Get the database object\n        database = self.get_database_object()\n        autodetect = self.collection_name in database.list_collection_names() and self.autodetect_collection\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": autodetect,\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params\n                    and self.collection_data(collection_name=self.collection_name, database=database) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Choose HybridSearchMode based on the selected param\n        hybrid_search_mode = HybridSearchMode.DEFAULT if self.search_method == \"Hybrid Search\" else HybridSearchMode.OFF\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=database.api_endpoint,\n                namespace=database.keyspace,\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Hybrid Search Parameters\n                hybrid_search=hybrid_search_mode,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except ValueError as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        # Add documents to the vector store\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        self.ingest_data = self._prepare_ingest_data()\n\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        documents = [\n            Document(\n                page_content=doc.page_content,\n                metadata=serialize(doc.metadata, to_str=True),\n            )\n            for doc in documents\n        ]\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=database.keyspace)\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except ValueError as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except ValueError as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        # Clean up the search query\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n        lexical_terms = self.lexical_terms or None\n\n        # Check if we have a search query, and if so set the args\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n                \"lexical_query\": lexical_terms,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n        self.log(f\"store.hybrid_search: {vector_store.hybrid_search}\")\n        self.log(f\"Lexical terms: {self.lexical_terms}\")\n        self.log(f\"Reranker: {self.reranker}\")\n\n        try:\n            search_args = self._build_search_args()\n        except ValueError as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except ValueError as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+                "value": "from astrapy import DataAPIClient\nfrom langchain_core.documents import Document\n\nfrom lfx_datastax.base.astradb_base import AstraDBBaseComponent\nfrom lfx.base.models.unified_models import (\n    apply_provider_variable_config_to_build_config,\n    get_embedding_model_options,\n    get_embeddings,\n    update_model_options_in_build_config,\n)\nfrom lfx.base.vectorstores.model import (\n    LCVectorStoreComponent,\n    check_cached_vector_store,\n)\nfrom lfx.base.vectorstores.vector_store_connection_decorator import (\n    vector_store_connection,\n)\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import (\n    BoolInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    ModelInput,\n    NestedDictInput,\n    QueryInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.serialization import serialize\nfrom lfx.utils.version import get_version_info\n\n\n@vector_store_connection\nclass AstraDBVectorStoreComponent(AstraDBBaseComponent, LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.langflow.org/bundles-datastax\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        *AstraDBBaseComponent.inputs,\n        *LCVectorStoreComponent.inputs,\n        ModelInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            info=\"Specify the Embedding Model. Not required for Astra Vectorize collections.\",\n            required=False,\n            show=True,\n            model_type=\"embedding\",\n            input_types=[\"Embeddings\"],\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"search_method\",\n            display_name=\"Search Method\",\n            info=(\n                \"Determine how your content is matched: Vector finds semantic similarity, \"\n                \"and Hybrid Search (suggested) combines both approaches \"\n                \"with a reranker.\"\n            ),\n            options=[\"Hybrid Search\", \"Vector Search\"],  # TODO: Restore Lexical Search?\n            options_metadata=[{\"icon\": \"SearchHybrid\"}, {\"icon\": \"SearchVector\"}],\n            value=\"Vector Search\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"reranker\",\n            display_name=\"Reranker\",\n            info=\"Post-retrieval model that re-scores results for optimal relevance ranking.\",\n            show=False,\n            toggle=True,\n        ),\n        QueryInput(\n            name=\"lexical_terms\",\n            display_name=\"Lexical Terms\",\n            info=\"Add additional terms/keywords to augment search precision.\",\n            placeholder=\"Enter terms to search...\",\n            separator=\" \",\n            show=False,\n            value=\"\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\n                \"Similarity\",\n                \"Similarity with score threshold\",\n                \"MMR (Max Marginal Relevance)\",\n            ],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n    ]\n\n    async def update_build_config(\n        self,\n        build_config: dict,\n        field_value: str | dict,\n        field_name: str | None = None,\n    ) -> dict:\n        \"\"\"Update build configuration with proper handling of embedding and search options.\"\"\"\n        # Update model options for ModelInput only when relevant.\n        # Only refresh when: initial load (None), embedding_model field changes, or api_key changes.\n        #\n        # Note: this intentionally uses update_model_options_in_build_config directly rather than\n        # handle_model_input_update because:\n        #   1. The model field is named \"embedding_model\" (not \"model\"), requiring model_field_name.\n        #   2. The refresh is conditional - we skip it for unrelated fields (e.g. database_name,\n        #      collection_name) to avoid unnecessary work.\n        #   3. AstraDB manages its own provider-field visibility (embedding_generation_provider\n        #      dialog), so the generic provider-field hide/show steps in handle_model_input_update\n        #      are not applicable here.\n        if field_name in (None, \"embedding_model\", \"api_key\"):\n            build_config = update_model_options_in_build_config(\n                component=self,\n                build_config=build_config,\n                cache_key_prefix=\"embedding_model_options\",\n                get_options_func=get_embedding_model_options,\n                field_name=field_name,\n                field_value=field_value if field_name == \"embedding_model\" else None,\n                model_field_name=\"embedding_model\",\n            )\n\n            # Auto-populate API key based on the selected embedding model's provider.\n            # Skip when user directly edits api_key to preserve their value.\n            if field_name != \"api_key\":\n                model_value = build_config.get(\"embedding_model\", {}).get(\"value\")\n                if isinstance(model_value, list) and model_value:\n                    provider = model_value[0].get(\"provider\", \"\")\n                    if provider:\n                        build_config = apply_provider_variable_config_to_build_config(build_config, provider)\n\n            # Ensure the API key field is always visible\n            if \"api_key\" in build_config:\n                build_config[\"api_key\"][\"show\"] = True\n\n        # Handle base astra db build config updates\n        build_config = await super().update_build_config(\n            build_config,\n            field_value=field_value,\n            field_name=field_name,\n        )\n\n        # Set embedding model display based on provider selection\n        if isinstance(field_value, dict) and \"02_embedding_generation_provider\" in field_value:\n            embedding_provider = field_value.get(\"02_embedding_generation_provider\")\n            is_custom_provider = embedding_provider and embedding_provider != \"Bring your own\"\n            provider = embedding_provider.lower() if is_custom_provider and embedding_provider is not None else None\n\n            build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n            build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Early return if no API endpoint is configured\n        if not self.get_api_endpoint():\n            return build_config\n\n        # Configure search method and related options\n        return self._configure_search_options(build_config)\n\n    def _configure_search_options(self, build_config: dict) -> dict:\n        \"\"\"Configure hybrid search, reranker, and vector search options.\"\"\"\n        # Detect available hybrid search capabilities\n        hybrid_capabilities = self._detect_hybrid_capabilities()\n\n        # Return if we haven't selected a collection\n        if not build_config[\"collection_name\"][\"options\"] or not build_config[\"collection_name\"][\"value\"]:\n            return build_config\n\n        # Get collection options\n        collection_options = self._get_collection_options(build_config)\n\n        # Get the selected collection index\n        index = build_config[\"collection_name\"][\"options\"].index(build_config[\"collection_name\"][\"value\"])\n        provider = build_config[\"collection_name\"][\"options_metadata\"][index][\"provider\"]\n        build_config[\"embedding_model\"][\"show\"] = not bool(provider)\n        build_config[\"embedding_model\"][\"required\"] = not bool(provider)\n\n        # Determine search configuration\n        is_vector_search = build_config[\"search_method\"][\"value\"] == \"Vector Search\"\n        is_autodetect = build_config[\"autodetect_collection\"][\"value\"]\n\n        # Apply hybrid search configuration\n        if hybrid_capabilities[\"available\"]:\n            build_config[\"search_method\"][\"show\"] = True\n            build_config[\"search_method\"][\"options\"] = [\n                \"Hybrid Search\",\n                \"Vector Search\",\n            ]\n            build_config[\"search_method\"][\"value\"] = build_config[\"search_method\"].get(\"value\", \"Hybrid Search\")\n\n            build_config[\"reranker\"][\"options\"] = hybrid_capabilities[\"reranker_models\"]\n            build_config[\"reranker\"][\"options_metadata\"] = hybrid_capabilities[\"reranker_metadata\"]\n            if hybrid_capabilities[\"reranker_models\"]:\n                build_config[\"reranker\"][\"value\"] = hybrid_capabilities[\"reranker_models\"][0]\n        else:\n            build_config[\"search_method\"][\"show\"] = False\n            build_config[\"search_method\"][\"options\"] = [\"Vector Search\"]\n            build_config[\"search_method\"][\"value\"] = \"Vector Search\"\n            build_config[\"reranker\"][\"options\"] = []\n            build_config[\"reranker\"][\"options_metadata\"] = []\n\n        # Configure reranker visibility and state\n        hybrid_enabled = (\n            collection_options[\"rerank_enabled\"] and build_config[\"search_method\"][\"value\"] == \"Hybrid Search\"\n        )\n\n        build_config[\"reranker\"][\"show\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_value\"] = hybrid_enabled\n        build_config[\"reranker\"][\"toggle_disable\"] = is_vector_search\n\n        # Configure lexical terms\n        lexical_visible = collection_options[\"lexical_enabled\"] and not is_vector_search\n        build_config[\"lexical_terms\"][\"show\"] = lexical_visible\n        build_config[\"lexical_terms\"][\"value\"] = \"\" if is_vector_search else build_config[\"lexical_terms\"][\"value\"]\n\n        # Configure search type and score threshold\n        build_config[\"search_type\"][\"show\"] = is_vector_search\n        build_config[\"search_score_threshold\"][\"show\"] = is_vector_search\n\n        # Force similarity search for hybrid mode or autodetect\n        if hybrid_enabled or is_autodetect:\n            build_config[\"search_type\"][\"value\"] = \"Similarity\"\n\n        return build_config\n\n    def _detect_hybrid_capabilities(self) -> dict:\n        \"\"\"Detect available hybrid search and reranking capabilities.\"\"\"\n        environment = self.get_environment(self.environment)\n        client = DataAPIClient(environment=environment)\n        admin_client = client.get_admin()\n        db_admin = admin_client.get_database_admin(self.get_api_endpoint(), token=self.token)\n\n        try:\n            providers = db_admin.find_reranking_providers()\n            reranker_models = [\n                model.name for provider_data in providers.reranking_providers.values() for model in provider_data.models\n            ]\n            reranker_metadata = [\n                {\"icon\": self.get_provider_icon(provider_name=model.name.split(\"/\")[0])}\n                for provider in providers.reranking_providers.values()\n                for model in provider.models\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Hybrid search not available: {e}\")\n            return {\n                \"available\": False,\n                \"reranker_models\": [],\n                \"reranker_metadata\": [],\n            }\n        else:\n            return {\n                \"available\": True,\n                \"reranker_models\": reranker_models,\n                \"reranker_metadata\": reranker_metadata,\n            }\n\n    def _get_collection_options(self, build_config: dict) -> dict:\n        \"\"\"Retrieve collection-level search options.\"\"\"\n        database = self.get_database_object(api_endpoint=build_config[\"api_endpoint\"][\"value\"])\n        collection = database.get_collection(\n            name=build_config[\"collection_name\"][\"value\"],\n            keyspace=build_config[\"keyspace\"][\"value\"],\n        )\n\n        col_options = collection.options()\n\n        return {\n            \"rerank_enabled\": bool(col_options.rerank and col_options.rerank.enabled),\n            \"lexical_enabled\": bool(col_options.lexical and col_options.lexical.enabled),\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import HybridSearchMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Resolve the embedding model from ModelInput selection to an Embeddings instance\n        embedding = None\n        if self.embedding_model:\n            embedding = get_embeddings(\n                model=self.embedding_model,\n                user_id=self.user_id,\n                api_key=getattr(self, \"api_key\", None),\n            )\n        embedding_params = {\"embedding\": embedding} if embedding else {}\n\n        # Get the additional parameters\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        # if os.getenv(\"AWS_EXECUTION_ENV\") == \"AWS_ECS_FARGATE\":  # TODO: More precise way of detecting\n        #     langflow_prefix = \"ds-\"\n\n        # Get the database object\n        database = self.get_database_object()\n        autodetect = self.collection_name in database.list_collection_names() and self.autodetect_collection\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": autodetect,\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params\n                    and self.collection_data(collection_name=self.collection_name, database=database) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Choose HybridSearchMode based on the selected param\n        hybrid_search_mode = HybridSearchMode.DEFAULT if self.search_method == \"Hybrid Search\" else HybridSearchMode.OFF\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=database.api_endpoint,\n                namespace=database.keyspace,\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Hybrid Search Parameters\n                hybrid_search=hybrid_search_mode,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except ValueError as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        # Add documents to the vector store\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        self.ingest_data = self._prepare_ingest_data()\n\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        documents = [\n            Document(\n                page_content=doc.page_content,\n                metadata=serialize(doc.metadata, to_str=True),\n            )\n            for doc in documents\n        ]\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=database.keyspace)\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except ValueError as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except ValueError as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        # Clean up the search query\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n        lexical_terms = self.lexical_terms or None\n\n        # Check if we have a search query, and if so set the args\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n                \"lexical_query\": lexical_terms,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n        self.log(f\"store.hybrid_search: {vector_store.hybrid_search}\")\n        self.log(f\"Lexical terms: {self.lexical_terms}\")\n        self.log(f\"Reranker: {self.reranker}\")\n\n        try:\n            search_args = self._build_search_args()\n        except ValueError as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except ValueError as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
               },
               "collection_name": {
                 "_input_type": "DropdownInput",
@@ -4559,6 +4559,7 @@
             "legacy": false,
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -4567,7 +4568,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -4576,8 +4577,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -4850,4 +4850,4 @@
     "q-a",
     "hybrid"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json
index bbc5b80c97..879fa7b391 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
@@ -429,11 +429,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -445,13 +446,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -601,7 +601,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1198,6 +1198,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1214,7 +1215,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -1227,8 +1228,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1605,6 +1605,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1613,7 +1614,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -1622,8 +1623,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -1933,11 +1933,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1949,13 +1950,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2105,7 +2105,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2684,4 +2684,4 @@
     "chatbots",
     "agents"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json
index 6f02fd2253..8bc5602c22 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json	
@@ -701,7 +701,7 @@
             "legacy": false,
             "lf_version": "1.10.1rc3",
             "metadata": {
-              "code_hash": "b41636ff3a48",
+              "code_hash": "53a28a3025de",
               "component_class_name": "KnowledgeComponent",
               "dependencies": {
                 "dependencies": [
@@ -841,7 +841,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Unified Knowledge component — ingest into or retrieve from a knowledge base.\n\nThis component merges what used to live in ``ingestion.py`` and\n``retrieval.py`` into a single, mode-driven component. A ``TabInput``\n(\"📥 Ingest\" / \"🔍 Retrieve\") drives which inputs and which output are\nvisible. The merged shape gives users one node per KB instead of two\nparallel ones that always shared the same ``knowledge_base`` picker,\nembedding-model metadata, and backend registry.\n\nBoth legacy classes (``KnowledgeIngestionComponent``,\n``KnowledgeBaseComponent``) remain importable as thin subclasses of\n``KnowledgeComponent`` — see the sibling ``ingestion.py`` / ``retrieval.py``\nmodules — so saved flows continue to load unchanged.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport uuid\nfrom collections.abc import Awaitable, Mapping\nfrom contextlib import aclosing\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, TypeVar\n\nimport anyio\nimport pandas as pd\n\nfrom lfx.base.knowledge_bases.backends import BackendType, BaseVectorStoreBackend\nfrom lfx.base.knowledge_bases.backends.base import BackendConfigurationError, IngestedDocument\nfrom lfx.base.knowledge_bases.backends.naming import ensure_storage_routing_allowed\nfrom lfx.base.knowledge_bases.ingestion_sources.base import (\n    IngestionItemResult,\n    IngestionItemStatus,\n    IngestionRunStatus,\n    IngestionSummary,\n)\nfrom lfx.base.knowledge_bases.ingestion_sources.flow_component import FlowComponentSource\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.knowledge_bases.validation import (\n    is_valid_collection_name as is_valid_kb_collection_name,\n)\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.helpers.base_model import coalesce_bool\nfrom lfx.io import (\n    BoolInput,\n    DBProviderInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MessageTextInput,\n    ModelInput,\n    Output,\n    SecretStrInput,\n    StrInput,\n    TabInput,\n    TableInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n    session_scope,\n)\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n\ndef _inputs_for_mode(default_mode: str) -> list:\n    \"\"\"Return a fresh copy of the canonical inputs list with show flags set for the given mode.\n\n    Used by the legacy subclasses so a saved flow keyed on\n    ``KnowledgeIngestion`` / ``KnowledgeBase`` lands on a node template\n    whose default visibility already matches the pinned mode — no\n    \"wait for the user to click the mode tab\" UX gap on load.\n    \"\"\"\n    always_visible = set(KnowledgeComponent.default_keys) | set(KnowledgeComponent.mode_config[default_mode])\n    inputs_copy = []\n    for inp in KnowledgeComponent.inputs:\n        clone = inp.model_copy(deep=True) if hasattr(inp, \"model_copy\") else inp\n        if clone.name == \"mode\":\n            clone.value = default_mode\n        else:\n            clone.show = clone.name in always_visible\n        inputs_copy.append(clone)\n    return inputs_copy\n\n\nif TYPE_CHECKING:\n    from pathlib import Path\n\n# Mode constants. Plain-text labels (no emoji) for consistency with the rest of\n# OpenXFlow's TabInput palette — see ``MemoryComponent`` for the same convention.\nMODE_INGEST = \"Ingest\"\nMODE_RETRIEVE = \"Retrieve\"\n\n# How often a flow ingestion checks whether its job was cancelled from the\n# Knowledge page while it waits on the embedding provider.\nJOB_CANCEL_POLL_SECONDS = 1.0\n\n\nclass KnowledgeIngestionCancelledError(Exception):\n    \"\"\"The ingestion's job was cancelled from the Knowledge page.\"\"\"\n\n\n_T = TypeVar(\"_T\")\n\n\nasync def _finish_despite_cancellation(awaitable: Awaitable[_T]) -> _T:\n    \"\"\"Run ``awaitable`` to completion even if the caller is cancelled meanwhile.\n\n    ``anyio.CancelScope(shield=True)`` holds off AnyIO cancellation, but a\n    repeated native ``Task.cancel()`` still interrupts it, and a stopped flow\n    build can cancel its task more than once. The work runs in its own task,\n    and the caller waits through every cancellation, re-raising once the work\n    has landed.\n    \"\"\"\n    task = asyncio.ensure_future(awaitable)\n    cancelled = False\n    with anyio.CancelScope(shield=True):\n        while not task.done():\n            try:\n                await asyncio.wait({task})\n            except asyncio.CancelledError:\n                cancelled = True\n    if cancelled:\n        error = None if task.cancelled() else task.exception()\n        if error is not None:\n            logger.warning(f\"Work finished during cancellation failed: {error!r}\")\n        raise asyncio.CancelledError from error\n    return task.result()\n\n\ndef _is_retrieve_mode(value: Any) -> bool:\n    \"\"\"Lenient mode check: treats any label containing 'Retrieve' as retrieve mode.\n\n    Older saved flows may carry the emoji-prefixed labels (\"📥 Ingest\" /\n    \"🔍 Retrieve\") this component used to ship with; substring matching\n    keeps those loading without forcing a flow rewrite.\n    \"\"\"\n    return isinstance(value, str) and \"Retrieve\" in value\n\n\n# Boolean flags of a ``column_config`` row. Toggled cells hold real booleans,\n# but a typed cell (or a flow saved while the cell rendered as plain text)\n# keeps the raw string, e.g. ``\"true\"`` — read them with ``coalesce_bool``.\n_COLUMN_FLAGS = (\"vectorize\", \"identifier\")\n\n\ndef _normalize_column_config(config_list: list[dict[str, Any]]) -> list[dict[str, Any]]:\n    \"\"\"Return copies of the rows with every flag as a real boolean.\"\"\"\n    return [{**row, **{key: coalesce_bool(row.get(key)) for key in _COLUMN_FLAGS}} for row in config_list]\n\n\n# Error message used by both the ingest and retrieve paths when the user is\n# running against an Astra cloud environment that disables these flows.\nastra_error_msg = \"Knowledge ingestion and retrieval are not supported in Astra cloud environment.\"\n\n_DEFAULT_OPENSEARCH_CONFIG = {\n    \"url_variable\": \"OPENSEARCH_URL\",\n    \"username_variable\": \"OPENSEARCH_USERNAME\",\n    \"password_variable\": \"OPENSEARCH_PASSWORD\",  # pragma: allowlist secret\n    \"index_name\": \"\",\n    \"vector_field\": \"vector_field\",\n    \"text_field\": \"text\",\n}\n\n\nclass KnowledgeComponent(Component):\n    \"\"\"One component for both writing into and reading from an OpenXFlow knowledge base.\n\n    A ``TabInput`` switches between ingestion and retrieval. The\n    ``update_build_config`` / ``update_outputs`` hooks hide the inputs\n    and the output that don't apply to the current mode so the canvas\n    node stays focused.\n    \"\"\"\n\n    display_name = \"Knowledge\"\n    description = \"Ingest into or retrieve from an OpenXFlow knowledge base.\"\n    icon = \"database\"\n    name = \"Knowledge\"\n\n    # ------ Mode → visible-fields wiring ---------------------------------\n    # ``default_keys`` are inputs always visible regardless of mode.\n    # ``mode_config`` lists the inputs unique to each mode; everything outside\n    # this set is hidden when its mode is not selected.\n    default_keys: list[str] = [\"mode\", \"knowledge_base\"]\n    mode_config: dict[str, list[str]] = {\n        MODE_INGEST: [\n            \"input_df\",\n            \"column_config\",\n            \"chunk_size\",\n            \"api_key\",\n            \"allow_duplicates\",\n            \"metadata_json\",\n        ],\n        MODE_RETRIEVE: [\n            \"search_query\",\n            \"top_k\",\n            \"include_metadata\",\n            \"include_embeddings\",\n            \"metadata_filter\",\n        ],\n    }\n\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self._cached_kb_path: Path | None = None\n        self._knowledge_output_result: Data | DataFrame | None = None\n\n    def _pre_run_setup(self) -> None:\n        self._knowledge_output_result = None\n\n    async def _get_output_result(self, output: Output):\n        # Both legacy handles dispatch to the selected mode. Terminal nodes\n        # evaluate both handles, but must execute the storage operation once.\n        if output.method not in {\"build_kb_info\", \"retrieve_data\"}:\n            return await super()._get_output_result(output)\n        if self._knowledge_output_result is None:\n            raw_output = output.model_copy(update={\"options\": None})\n            self._knowledge_output_result = await super()._get_output_result(raw_output)\n        output.value = output.apply_options(self._knowledge_output_result)\n        return output.value\n\n    @staticmethod\n    def _embedding_provider_from_selection(selection: Any) -> str | None:\n        \"\"\"Read a provider name from a raw or persisted model selection.\"\"\"\n        if isinstance(selection, list):\n            selection = selection[0] if selection else None\n        if not isinstance(selection, Mapping):\n            return None\n        provider = selection.get(\"provider\")\n        if not isinstance(provider, str) or not provider.strip():\n            return None\n        return provider.strip()\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Resolve the embedding provider before dialog hooks or runtime secrets.\n\n        New-KB configuration carries its ModelInput inside a dropdown dialog,\n        while existing KBs persist the provider in the KB row. Neither is a\n        top-level ModelInput, so both must be resolved explicitly before the\n        generic component pipeline hydrates ``api_key``.\n        \"\"\"\n        from lfx.base.models.provider_registry import resolve_provider_id\n        from lfx.services.model_provider_policy import ModelProviderPolicyError\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        knowledge_value = effective_parameters.get(\"knowledge_base\", getattr(self, \"knowledge_base\", None))\n\n        if isinstance(knowledge_value, Mapping):\n            if \"02_embedding_model\" not in knowledge_value:\n                return ()\n            provider = self._embedding_provider_from_selection(knowledge_value.get(\"02_embedding_model\"))\n        elif isinstance(knowledge_value, str) and knowledge_value.strip():\n            metadata = await self._get_kb_metadata(knowledge_value.strip())\n            provider = self._embedding_provider_from_selection(metadata.get(\"model_selection\"))\n            if provider is None:\n                legacy_provider = metadata.get(\"embedding_provider\")\n                provider = legacy_provider.strip() if isinstance(legacy_provider, str) else None\n                if provider == \"Unknown\":\n                    provider = None\n        else:\n            return ()\n\n        if provider is None:\n            unknown_provider = \"unknown\"\n            raise ModelProviderPolicyError(unknown_provider, purpose)\n        return (resolve_provider_id(provider),)\n\n    @dataclass\n    class NewKnowledgeBaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"name\": \"create_knowledge_base\",\n                        \"description\": \"Create new knowledge in OpenXFlow.\",\n                        \"display_name\": \"Create new Knowledge Base\",\n                        \"field_order\": [\n                            \"01_new_kb_name\",\n                            \"02_embedding_model\",\n                            \"03_knowledge_backend\",\n                        ],\n                        \"template\": {\n                            \"01_new_kb_name\": StrInput(\n                                name=\"new_kb_name\",\n                                display_name=\"Knowledge Name\",\n                                info=\"Name of the new knowledge to create.\",\n                                required=True,\n                            ),\n                            \"02_embedding_model\": ModelInput(\n                                name=\"embedding_model\",\n                                display_name=\"Choose Embedding Model\",\n                                info=(\n                                    \"Select the embedding model to use for this knowledge base. \"\n                                    \"OpenXFlow uses the configured credentials for that model provider.\"\n                                ),\n                                required=True,\n                                model_type=\"embedding\",\n                            ),\n                            \"03_knowledge_backend\": DBProviderInput(\n                                name=\"knowledge_backend\",\n                                display_name=\"DB Provider\",\n                                info=(\n                                    \"Select where this knowledge base stores vectors. \"\n                                    \"OpenSearch uses the global DB Providers settings.\"\n                                ),\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    # ------ Inputs --------------------------------------------------------\n    # ``knowledge_base`` is kept at position 0 so legacy tests that check\n    # ``component.inputs[0].dialog_inputs`` continue to work.\n    inputs = [\n        DropdownInput(\n            name=\"knowledge_base\",\n            display_name=\"Knowledge\",\n            info=\"Select the knowledge to load data from.\",\n            required=True,\n            options=[],\n            refresh_button=True,\n            real_time_refresh=True,\n            dialog_inputs=asdict(NewKnowledgeBaseInput()),\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[MODE_INGEST, MODE_RETRIEVE],\n            value=MODE_INGEST,\n            info=\"Switch between writing new data into the knowledge base and querying it.\",\n            real_time_refresh=True,\n            tool_mode=True,\n        ),\n        # --- Ingest-only inputs (default-shown; hidden when mode == Retrieve) -\n        HandleInput(\n            name=\"input_df\",\n            display_name=\"Input\",\n            info=(\n                \"Table with all original columns (already chunked / processed). \"\n                \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n                \"it is converted to a DataFrame automatically.\"\n            ),\n            input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n            required=True,\n            dynamic=True,\n            show=True,\n        ),\n        TableInput(\n            name=\"column_config\",\n            display_name=\"Column Configuration\",\n            info=\"Configure column behavior for the knowledge base.\",\n            required=True,\n            table_schema=[\n                {\n                    \"name\": \"column_name\",\n                    \"display_name\": \"Column Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Name of the column in the source DataFrame\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"vectorize\",\n                    \"display_name\": \"Vectorize\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Create embeddings for this column\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"identifier\",\n                    \"display_name\": \"Identifier\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Use this column as unique identifier\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"column_name\": \"text\",\n                    \"vectorize\": True,\n                    \"identifier\": True,\n                },\n            ],\n            dynamic=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"Batch size for processing embeddings\",\n            advanced=True,\n            value=1000,\n            dynamic=True,\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Embedding Provider API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        BoolInput(\n            name=\"allow_duplicates\",\n            display_name=\"Allow Duplicates\",\n            info=\"Allow duplicate rows in the knowledge base\",\n            advanced=True,\n            value=False,\n            dynamic=True,\n            show=True,\n        ),\n        StrInput(\n            name=\"metadata_json\",\n            display_name=\"Metadata\",\n            info=(\n                \"Optional JSON object of user metadata applied to every chunk produced by this \"\n                'run (e.g. {\"tag\": \"invoice\", \"year\": \"2026\"}). Same shape as the upload modal '\n                \"Metadata section so chunks browser filters + Knowledge retrieval metadata_filter \"\n                \"work uniformly across upload, folder, and flow-driven ingestion. Malformed JSON is \"\n                \"ignored with a warning rather than failing the run.\"\n            ),\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        # --- Retrieve-only inputs (default-hidden; shown when mode == Retrieve) -\n        MessageTextInput(\n            name=\"search_query\",\n            display_name=\"Search Query\",\n            info=\"Optional search query to filter knowledge base data.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of top results to return from the knowledge base.\",\n            value=5,\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_metadata\",\n            display_name=\"Include Metadata\",\n            info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n            value=True,\n            advanced=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_embeddings\",\n            display_name=\"Include Embeddings\",\n            info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n            value=False,\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"metadata_filter\",\n            display_name=\"Metadata Filter\",\n            info=(\n                \"Optional JSON object of user-metadata key/value pairs. Only chunks \"\n                'whose source_metadata matches every key are returned (e.g. {\"tag\": \"invoice\"} '\n                'or {\"tag\": [\"invoice\", \"audit\"]} for OR-of-values). Backends without '\n                \"native filtering apply the match client-side after retrieval.\"\n            ),\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n    ]\n\n    # ------ Outputs -------------------------------------------------------\n    # Both outputs are declared at the class level so the runtime can\n    # dispatch to either method depending on which one the saved flow has\n    # wired up. ``update_outputs`` filters the canvas-visible output per\n    # selected mode; see ``TypeConverterComponent`` and ``MemoryComponent``\n    # for the same pattern.\n    #\n    # Output names match the legacy ``KnowledgeIngestionComponent``\n    # (``dataframe_output``) and ``KnowledgeBaseComponent``\n    # (``retrieve_data``) so saved flow edges keyed on those names resolve\n    # cleanly against the merged component.\n    outputs = [\n        Output(\n            display_name=\"Results\",\n            name=\"dataframe_output\",\n            method=\"build_kb_info\",\n            types=[\"JSON\"],\n            selected=\"JSON\",\n        ),\n        Output(\n            display_name=\"Results\",\n            name=\"retrieve_data\",\n            method=\"retrieve_data\",\n            info=\"Returns the data from the selected knowledge base.\",\n            types=[\"Table\"],\n            selected=\"Table\",\n        ),\n    ]\n\n    # ------ Mode-driven UI updates ---------------------------------------\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"Sync the visible output with the current ``mode`` value on canvas load.\n\n        Saved flows hit this path: ``update_outputs`` is normally only triggered\n        by ``real_time_refresh`` field edits, so without this re-sync the\n        canvas could land with both outputs visible.\n        \"\"\"\n        await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = new_frontend_node.get(\"template\", {})\n        mode_value = template.get(\"mode\", {}).get(\"value\", MODE_INGEST)\n        self.update_outputs(new_frontend_node, \"mode\", mode_value)\n        # The rebuilt template carries class-default (Ingest) ``show`` flags,\n        # so a saved Retrieve node returned ``search_query`` hidden and the canvas\n        # silently dropped the edge feeding it.\n        self._apply_mode_visibility(template)\n        return new_frontend_node\n\n    def _apply_mode_visibility(self, build_config, field_name: str | None = None, field_value: Any = None):\n        \"\"\"Show only the inputs that belong to the effective mode.\"\"\"\n        current_mode = build_config.get(\"mode\", {}).get(\"value\") if isinstance(build_config, dict) else None\n        if field_name == \"mode\":\n            current_mode = field_value\n        # Map legacy/emoji-prefixed labels onto the current canonical values so\n        # flows saved before the label change still toggle visibility correctly.\n        if _is_retrieve_mode(current_mode):\n            current_mode = MODE_RETRIEVE\n        elif current_mode not in self.mode_config:\n            current_mode = MODE_INGEST\n        return set_current_fields(\n            build_config=build_config if isinstance(build_config, dotdict) else dotdict(build_config),\n            action_fields=self.mode_config,\n            selected_action=current_mode,\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Filter visible outputs to match the selected mode.\n\n        Triggered by the ``mode`` ``TabInput`` ``real_time_refresh`` flag.\n        The class-level ``outputs`` list always carries both entries so the\n        runtime can resolve either ``build_kb_info`` or ``retrieve_data``\n        regardless of canvas visibility — we just hide the unused one here.\n        \"\"\"\n        if field_name != \"mode\":\n            return frontend_node\n        if _is_retrieve_mode(field_value):\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"retrieve_data\",\n                    method=\"retrieve_data\",\n                    info=\"Returns the data from the selected knowledge base.\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                )\n            ]\n        else:\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"dataframe_output\",\n                    method=\"build_kb_info\",\n                    types=[\"JSON\"],\n                    selected=\"JSON\",\n                )\n            ]\n        return frontend_node\n\n    async def update_build_config(\n        self,\n        build_config,\n        field_value: Any,\n        field_name: str | None = None,\n    ):\n        \"\"\"Refresh KB options, drive the create-KB dialog, and hide off-mode fields.\"\"\"\n        # Astra-cloud gate covers both ingest and retrieve paths.\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        # Always populate the create-KB dialog's embedding-model options so the\n        # ModelInput renders correctly regardless of which input triggered the\n        # refresh.\n        try:\n            dialog_template = (\n                build_config[\"knowledge_base\"]\n                .get(\"dialog_inputs\", {})\n                .get(\"fields\", {})\n                .get(\"data\", {})\n                .get(\"node\", {})\n                .get(\"template\", {})\n            )\n            if \"02_embedding_model\" in dialog_template:\n                embedding_options = get_embedding_model_options(user_id=self.user_id)\n                dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n        except Exception:  # noqa: BLE001\n            self.log(\"Failed to populate embedding model options in dialog\")\n\n        # KB-picker refresh + create-new-KB flow (lifted from the legacy ingestion\n        # component verbatim; relied on by both the canvas refresh button and the\n        # dialog-submit path).\n        if field_name == \"knowledge_base\":\n            # Lazy import keeps lfx importable without langflow installed.\n            from langflow.services.database.models.user.crud import get_user_by_id\n\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for fetching knowledge base list.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n                if not current_user:\n                    msg = f\"User with ID {self.user_id} not found.\"\n                    raise ValueError(msg)\n            if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n                model_selection = field_value[\"02_embedding_model\"]\n                if isinstance(model_selection, dict):\n                    model_selection = [model_selection]\n\n                backend_type, backend_config = self._normalize_backend_selection(\n                    field_value.get(\"03_knowledge_backend\")\n                )\n                ensure_storage_routing_allowed(backend_config, is_superuser=bool(current_user.is_superuser))\n                new_kb_name = field_value[\"01_new_kb_name\"]\n                from langflow.api.utils.kb_helpers import local_chroma_rejection_reason, validate_kb_name\n\n                validate_kb_name(new_kb_name)\n                rejection = local_chroma_rejection_reason(backend_type, backend_config)\n                if rejection:\n                    raise ValueError(rejection)\n\n                embed_model = get_embeddings(\n                    model=model_selection,\n                    user_id=self.user_id,\n                )\n\n                try:\n                    await asyncio.wait_for(\n                        asyncio.to_thread(embed_model.embed_query, \"test\"),\n                        timeout=10,\n                    )\n                except TimeoutError as e:\n                    msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n                    raise ValueError(msg) from e\n                except Exception as e:\n                    msg = f\"Embedding validation failed: {e!s}\"\n                    raise ValueError(msg) from e\n\n                build_config[\"knowledge_base\"][\"value\"] = new_kb_name\n                await self._create_knowledge_base_record(\n                    user_id=self.user_id,\n                    name=field_value[\"01_new_kb_name\"],\n                    model_selection=model_selection,\n                    backend_type=backend_type,\n                    backend_config=backend_config,\n                )\n\n            build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(user_id=self.user_id)\n            if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n                build_config[\"knowledge_base\"][\"value\"] = None\n\n        # Honor the current mode regardless of which field triggered the refresh.\n        return self._apply_mode_visibility(build_config, field_name, field_value)\n\n    # =====================================================================\n    #                       INGESTION CODE PATH\n    # =====================================================================\n    @staticmethod\n    def _scalar_notna(value) -> bool:\n        \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n        ``pd.notna`` returns an array when given an array-like input, which\n        cannot be used directly in a boolean context.  This helper collapses\n        the result to a single scalar ``bool``.\n        \"\"\"\n        result = pd.notna(value)\n        if hasattr(result, \"__iter__\") and not isinstance(result, str):\n            import numpy as np\n\n            arr = np.asarray(result)\n            return arr.size > 0 and arr.all()\n        return bool(result)\n\n    def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n        \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n        if not self.column_config:\n            msg = \"Column configuration cannot be empty\"\n            raise ValueError(msg)\n\n        config_list = self.column_config if isinstance(self.column_config, list) else []\n\n        df_columns = set(df_source.columns)\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            if col_name not in df_columns:\n                msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n                raise ValueError(msg)\n\n        return config_list\n\n    @staticmethod\n    def _extract_source_types_from_df(df_source: pd.DataFrame) -> set[str]:\n        \"\"\"Pull file extensions out of common path/name columns on the source DataFrame.\n\n        The direct-upload ingestion path stores extensions in\n        the KB row's ``source_types`` so the KB list can render the\n        correct file-type icon. When ingestion happens via a connected\n        ``input_df`` (e.g. File → Knowledge) the same field stayed empty and\n        the icon defaulted to a blank tile. We look at the well-known columns\n        the File / S3 / cloud-storage components produce and collect any\n        plausible extension so the icon is consistent across both flows.\n        \"\"\"\n        candidate_columns = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for col in candidate_columns:\n            if col not in df_source.columns:\n                continue\n            for value in df_source[col].dropna():\n                ext = KnowledgeComponent._extension_from_value(value)\n                # Drop anything that doesn't look like an extension (URL\n                # query strings, version segments, etc.) — the icon palette\n                # keys off short alphanumeric tokens like \"pdf\"/\"docx\".\n                if ext:\n                    extensions.add(ext)\n        return extensions\n\n    @staticmethod\n    def _extension_from_value(value: Any) -> str | None:\n        \"\"\"Return a normalized file-extension token from a path / filename / MIME string.\n\n        Accepts values like ``\"report.PDF\"``, ``\"/docs/notes.txt\"``, or\n        ``\"application/pdf\"`` and returns ``\"pdf\"`` / ``\"txt\"``. Returns\n        ``None`` if no plausible extension can be derived.\n        \"\"\"\n        extension_length_limit = 10\n        if value is None:\n            return None\n        text = str(value).strip()\n        if not text:\n            return None\n        # MIME types like ``application/pdf`` carry the canonical extension\n        # in the subtype slot — preserve them so File / S3 messages keyed\n        # only on ``mimetype`` still resolve to an icon.\n        if \"/\" in text and \".\" not in text.rsplit(\"/\", 1)[-1]:\n            subtype = text.rsplit(\"/\", 1)[-1].strip().lower()\n            return subtype if subtype and len(subtype) <= extension_length_limit and subtype.isalnum() else None\n        if \".\" not in text:\n            return None\n        ext = text.rsplit(\".\", 1)[-1].strip().lower()\n        if ext and len(ext) <= extension_length_limit and ext.isalnum():\n            return ext\n        return None\n\n    @classmethod\n    def _extract_source_types_from_mapping(cls, mapping: Any) -> set[str]:\n        \"\"\"Pull extensions from a Message/Data-style ``data`` dict or a plain dict.\"\"\"\n        if not isinstance(mapping, dict):\n            return set()\n        candidate_keys = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for key in candidate_keys:\n            ext = cls._extension_from_value(mapping.get(key))\n            if ext:\n                extensions.add(ext)\n        return extensions\n\n    @classmethod\n    def _extract_source_types_from_input(cls, input_value: Any) -> set[str]:\n        \"\"\"Pull file extensions out of the raw component input.\n\n        ``convert_to_dataframe`` strips Message/Data fields down to ``text``\n        when projecting onto a DataFrame, so file metadata attached to a\n        File-component \"Raw Content\" output never reaches\n        ``_extract_source_types_from_df``. Looking at the raw input first\n        keeps the KB icon consistent with direct upload.\n        \"\"\"\n        if input_value is None:\n            return set()\n        if isinstance(input_value, list):\n            extensions: set[str] = set()\n            for item in input_value:\n                extensions |= cls._extract_source_types_from_input(item)\n            return extensions\n        if isinstance(input_value, pd.DataFrame):\n            return cls._extract_source_types_from_df(input_value)\n        # Message / Data / JSON all expose a ``data`` dict via the lfx schema.\n        mapping = getattr(input_value, \"data\", None)\n        if mapping is None and isinstance(input_value, dict):\n            mapping = input_value\n        return cls._extract_source_types_from_mapping(mapping)\n\n    async def _refresh_kb_stats(\n        self,\n        *,\n        kb_record_id: Any,\n        backend: BaseVectorStoreBackend,\n        extensions: set[str],\n    ) -> None:\n        \"\"\"Recount the KB from its vector store and write the totals onto the row.\n\n        Backend-agnostic: counts come from ``count`` / ``iter_documents`` /\n        ``storage_size_bytes``, so a Chroma, OpenSearch, or pgVector KB all report\n        real numbers. ``extensions`` are merged into ``source_types`` so the KB\n        list renders the right file-type icon regardless of which ingestion route\n        produced the chunks.\n\n        Best-effort: the chunks are already written and committed by the time this\n        runs, so a stats refresh failure must not fail the ingestion.\n        \"\"\"\n        if kb_record_id is None:\n            return\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return\n\n        try:\n            chunks = await backend.count()\n            characters = 0\n            words = 0\n            async with aclosing(backend.iter_documents()) as batches:\n                async for batch in batches:\n                    for document in batch:\n                        characters += len(document.content)\n                        words += len(document.content.split())\n\n            record = await knowledge_base_service.get_by_id(kb_record_id)\n            existing = set(record.source_types or []) if record is not None else set()\n\n            await knowledge_base_service.update_stats(\n                kb_record_id,\n                chunks=chunks,\n                words=words,\n                characters=characters,\n                size_bytes=await backend.storage_size_bytes(),\n                source_types=sorted(existing | extensions),\n            )\n        except Exception as e:  # noqa: BLE001 — stats are cosmetic; chunks are already written\n            self.log(f\"Warning: Could not refresh knowledge base stats: {e}\")\n\n    @staticmethod\n    def _default_backend_selection() -> tuple[str, dict[str, Any]]:\n        \"\"\"Deployment default for a new KB with no explicit backend chosen.\n\n        Environment-driven: pgVector when ``PGVECTOR_CONNECTION_STRING`` is set,\n        else OpenSearch when ``OPENSEARCH_URL`` is set, else SQLite. An\n        unspecified selection (headless flows, multi-replica) therefore lands on\n        the deployment's shared store.\n        \"\"\"\n        from lfx.base.knowledge_bases.backends.postgres import resolve_default_kb_backend\n\n        backend_type = resolve_default_kb_backend()\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, dict(_DEFAULT_OPENSEARCH_CONFIG)\n        return backend_type, {}\n\n    @classmethod\n    def _normalize_backend_selection(cls, value: Any) -> tuple[str, dict[str, Any]]:\n        \"\"\"Normalize a DBProviderInput value into backend type/config.\"\"\"\n        if not value:\n            return cls._default_backend_selection()\n\n        backend_config: dict[str, Any]\n        if isinstance(value, str):\n            backend_type, backend_config = value, {}\n        elif isinstance(value, dict):\n            backend_type = str(value.get(\"backend_type\") or value.get(\"id\") or BackendType.SQLITE.value)\n            backend_config = value.get(\"backend_config\") or value.get(\"config\") or {}\n        else:\n            msg = \"Invalid vector-store backend selection.\"\n            raise TypeError(msg)\n        if not isinstance(backend_config, dict):\n            msg = \"Vector-store backend configuration must be an object.\"\n            raise TypeError(msg)\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, {**_DEFAULT_OPENSEARCH_CONFIG, **backend_config}\n        if backend_type in {BackendType.SQLITE.value, BackendType.POSTGRES.value}:\n            return backend_type, backend_config\n        msg = \"Chroma is retired. Choose SQLite, pgVector, or OpenSearch for a new knowledge base.\"\n        raise ValueError(msg)\n\n    async def _create_knowledge_base_record(\n        self,\n        *,\n        user_id: Any,\n        name: str,\n        model_selection: list[dict[str, Any]],\n        backend_type: str,\n        backend_config: dict[str, Any],\n    ) -> None:\n        \"\"\"Persist the component-created KB in the DB when OpenXFlow is available.\"\"\"\n        from langflow.api.utils import knowledge_base_service\n\n        await knowledge_base_service.create_record(\n            user_id=user_id,\n            name=name,\n            model_selection=model_selection,\n            column_config=_normalize_column_config(self.column_config) if isinstance(self.column_config, list) else [],\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n        \"\"\"Build detailed column metadata.\"\"\"\n        metadata: dict[str, Any] = {\n            \"total_columns\": len(df_source.columns),\n            \"mapped_columns\": len(config_list),\n            \"unmapped_columns\": len(df_source.columns) - len(config_list),\n            \"columns\": [],\n            \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n        }\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            vectorize = coalesce_bool(config.get(\"vectorize\"))\n            identifier = coalesce_bool(config.get(\"identifier\"))\n\n            metadata[\"columns\"].append(\n                {\n                    \"name\": col_name,\n                    \"vectorize\": vectorize,\n                    \"identifier\": identifier,\n                }\n            )\n\n            if vectorize:\n                metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n            if identifier:\n                metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n        return metadata\n\n    async def _create_vector_store(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        \"\"\"Create vector store using the configured DB provider.\"\"\"\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            return await self._ingest_into_backend(backend, df_source, config_list, embedding_function, job_id=job_id)\n        except BaseException:\n            await _finish_despite_cancellation(backend.teardown())\n            raise\n\n    async def _ingest_into_backend(\n        self,\n        backend: BaseVectorStoreBackend,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        await backend.ensure_ready()\n\n        from langflow.services.knowledge_base_storage.runtime import operation, resolve_record\n\n        record = await resolve_record(self._user_uuid, self.knowledge_base)\n        async with operation(record, shared=True):\n            existing_ids = set()\n            if not self.allow_duplicates:\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        for document in batch:\n                            doc_id = document.metadata.get(\"_id\")\n                            if doc_id:\n                                existing_ids.add(doc_id)\n\n        data_objects = await self._convert_df_to_data_objects(df_source, config_list, existing_ids=existing_ids)\n\n        user_metadata_tag = self._resolve_user_metadata_tag()\n\n        documents = []\n        for data_obj in data_objects:\n            doc = data_obj.to_lc_document()\n            if user_metadata_tag:\n                doc.metadata[\"source_metadata\"] = user_metadata_tag\n            documents.append(doc)\n        if not documents:\n            return backend\n\n        # No storage lease may surround the provider call. The write lease\n        # below rechecks routing and IDs after concurrent operations finish.\n        vectors = await self._embed_unless_job_cancelled(\n            embedding_function, [doc.page_content for doc in documents], job_id\n        )\n        if len(vectors) != len(documents):\n            msg = \"Embedding provider returned an incorrect number of vectors\"\n            raise BackendConfigurationError(msg)\n        embedded = [\n            IngestedDocument(doc.page_content, doc.metadata, vector, id=doc.id)\n            for doc, vector in zip(documents, vectors, strict=True)\n        ]\n        async with operation(record):\n            # A cancel that landed after the embeddings returned must still\n            # keep this run's chunks out of the KB.\n            await self._raise_if_job_cancelled(job_id)\n            if not self.allow_duplicates:\n                current_ids = set()\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        current_ids.update(doc.metadata[\"_id\"] for doc in batch if doc.metadata.get(\"_id\"))\n                unique = []\n                for doc in embedded:\n                    doc_id = doc.metadata.get(\"_id\")\n                    if doc_id and doc_id in current_ids:\n                        continue\n                    if doc_id:\n                        current_ids.add(doc_id)\n                    unique.append(doc)\n                embedded = unique\n\n            if embedded:\n                await backend.add_embedded_documents(embedded)\n                self.log(f\"Added {len(embedded)} documents to vector store '{self.knowledge_base}'\")\n\n        return backend\n\n    async def _embed_unless_job_cancelled(\n        self, embedding_function, texts: list[str], job_id: uuid.UUID | None\n    ) -> list[list[float]]:\n        \"\"\"Embed ``texts``, giving up as soon as the run's job is cancelled.\n\n        The Knowledge page cancels a run by marking its job. The embedding call\n        is the long, write-free part of a flow ingestion, so it is the safe\n        place to stop.\n        \"\"\"\n        await self._raise_if_job_cancelled(job_id)\n        if job_id is None:\n            return await embedding_function.aembed_documents(texts)\n\n        # A task group would wrap provider errors in an ExceptionGroup, so a\n        # plain watcher task cancels this scope instead.\n        with anyio.CancelScope() as scope:\n            watcher = asyncio.ensure_future(self._cancel_when_job_cancelled(job_id, scope))\n            try:\n                return await embedding_function.aembed_documents(texts)\n            finally:\n                # The watcher may be mid-poll; let its DB session close.\n                watcher.cancel()\n                with anyio.CancelScope(shield=True):\n                    await asyncio.wait({watcher})\n        raise self._cancelled_error()\n\n    async def _cancel_when_job_cancelled(self, job_id: uuid.UUID, scope: anyio.CancelScope) -> None:\n        # The cancel is a job-row write from another request, so it is polled.\n        while True:\n            await asyncio.sleep(JOB_CANCEL_POLL_SECONDS)\n            if await self._job_cancelled(job_id):\n                scope.cancel()\n                return\n\n    async def _raise_if_job_cancelled(self, job_id: uuid.UUID | None) -> None:\n        if await self._job_cancelled(job_id):\n            raise self._cancelled_error()\n\n    def _cancelled_error(self) -> KnowledgeIngestionCancelledError:\n        return KnowledgeIngestionCancelledError(f\"Ingestion into '{self.knowledge_base}' was cancelled.\")\n\n    async def _job_cancelled(self, job_id: uuid.UUID | None) -> bool:\n        if job_id is None:\n            return False\n        try:\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError:\n            return False\n        try:\n            job = await get_job_service().get_job_by_job_id(job_id)\n        except Exception as exc:  # noqa: BLE001 — a failed poll must not abort the ingestion\n            self.log(f\"Could not check whether ingestion job {job_id} was cancelled: {exc}\")\n            return False\n        return job is not None and job.status == JobStatus.CANCELLED\n\n    async def _convert_df_to_data_objects(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        existing_ids: set[str] | None = None,\n    ) -> list[Data]:\n        \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n        data_objects: list[Data] = []\n\n        if existing_ids is None:\n            existing_ids = set()\n\n        content_cols = []\n        identifier_cols = []\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n\n            if coalesce_bool(config.get(\"vectorize\")):\n                content_cols.append(col_name)\n            if coalesce_bool(config.get(\"identifier\")):\n                identifier_cols.append(col_name)\n\n        for _, row in df_source.iterrows():\n            identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n            page_content = \" \".join(identifier_parts)\n\n            data_dict = {\n                \"text\": page_content,\n            }\n\n            if identifier_cols:\n                identifier_parts = [\n                    str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n                ]\n                page_content = \" \".join(identifier_parts)\n\n            for col in df_source.columns:\n                if col not in content_cols and col in row and self._scalar_notna(row[col]):\n                    value = row[col]\n                    data_dict[col] = str(value)\n\n            page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n            data_dict[\"_id\"] = page_content_hash\n\n            if not self.allow_duplicates and page_content_hash in existing_ids:\n                self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n                continue\n\n            data_obj = Data(data=data_dict)\n            data_objects.append(data_obj)\n\n        return data_objects\n\n    def is_valid_collection_name(self, name: str) -> bool:\n        \"\"\"Return whether ``name`` satisfies the shared collection-name contract.\"\"\"\n        return is_valid_kb_collection_name(name)\n\n    def _resolve_store_path(\n        self,\n        kb_user: str,\n        backend_type: str,\n        backend_config: dict[str, Any] | None,\n    ) -> Path | None:\n        \"\"\"Return this KB's on-disk directory, or ``None`` when it needs no disk.\n\n        Only local Chroma stores anything on this box; every other backend\n        resolves to ``None`` and never touches the filesystem.\n        \"\"\"\n        # Lazy import keeps lfx importable without langflow's DB services.\n        from langflow.api.utils.kb_helpers import resolve_local_store_path\n\n        return resolve_local_store_path(\n            self.knowledge_base,\n            kb_user,\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    async def _kb_username(self) -> str:\n        \"\"\"Resolve the owning user's username (used to root a local-Chroma path).\"\"\"\n        cached = getattr(self, \"_cached_kb_username\", None)\n        if cached is not None:\n            return cached\n\n        # Lazy import to keep ``lfx`` importable standalone — langflow's\n        # user/DB models are not always available at module load time.\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        async with session_scope() as db:\n            if not self.user_id:\n                msg = \"User ID is required for fetching knowledge base path.\"\n                raise ValueError(msg)\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n            self._cached_kb_username = current_user.username\n\n        return self._cached_kb_username\n\n    async def _kb_path(self) -> Path | None:\n        \"\"\"Resolve the KB's local directory from its configured backend.\"\"\"\n        cached_path = getattr(self, \"_cached_kb_path\", None)\n        if cached_path is not None:\n            return cached_path\n\n        backend_type, backend_config = await self._resolve_backend_config()\n        kb_user = await self._kb_username()\n        self._cached_kb_path = self._resolve_store_path(kb_user, backend_type, backend_config)\n        return self._cached_kb_path\n\n    def _resolve_user_metadata_tag(self) -> str:\n        \"\"\"Return the JSON-encoded user metadata tag for chunk writes.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return \"\"\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return \"\"\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError) as exc:\n            self.log(f\"KnowledgeComponent: metadata_json is not valid JSON ({exc}); skipping metadata stamp.\")\n            return \"\"\n        if not isinstance(decoded, dict):\n            self.log(\"KnowledgeComponent: metadata_json must decode to a JSON object; skipping metadata stamp.\")\n            return \"\"\n        return json.dumps(decoded, sort_keys=True)\n\n    async def build_kb_info(self) -> Data:\n        \"\"\"Main ingestion routine → returns a dict with KB metadata.\n\n        The annotation is intentionally narrowed to ``Data`` even though the\n        cross-mode fallback below may return a ``DataFrame`` from\n        ``retrieve_data``. The frontend builds React-Flow handle IDs from\n        this output's type list; widening it to ``Data | DataFrame`` makes\n        the API advertise ``[\"JSON\", \"Table\"]`` for the ingest output and\n        breaks every saved-edge sourceHandle that was generated against\n        a single-type handle (BUG-02). Python doesn't enforce return\n        annotations at runtime, so the rare fallback path keeps working.\n        \"\"\"\n        if _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.retrieve_data()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        run_id: uuid.UUID | None = None\n        run_job_id: uuid.UUID | None = None\n        run_summary: IngestionSummary | None = None\n        run_status: IngestionRunStatus = IngestionRunStatus.SUCCEEDED\n        run_error: str | None = None\n        kb_record_id: uuid.UUID | None = None\n        previous_kb_status = \"ready\"\n        previous_failure_reason: str | None = None\n        try:\n            input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n            df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n            config_list = self._validate_column_config(df_source)\n            column_metadata = self._build_column_metadata(config_list, df_source)\n\n            # Embedding config comes from the ``knowledge_base`` row — the sole\n            # authority. No sidecar is read, so ingestion resolves the same model\n            # on any replica.\n            stored_metadata = await self._get_kb_metadata()\n            # A cancelled run restores this status. Only an earlier failure is\n            # kept; \"empty\" is an API projection of a ready KB with no chunks.\n            if stored_metadata.get(\"status\") == \"failed\":\n                previous_kb_status = \"failed\"\n                previous_failure_reason = stored_metadata.get(\"failure_reason\")\n            model_selection = stored_metadata.get(\"model_selection\")\n            if model_selection:\n                model_selection = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            else:\n                embedding_model_name = stored_metadata.get(\"embedding_model\")\n                embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n                if embedding_model_name:\n                    try:\n                        all_options = get_embedding_model_options(user_id=self.user_id)\n                        match = next(\n                            (o for o in all_options if o.get(\"name\") == embedding_model_name),\n                            None,\n                        )\n                        if match:\n                            model_selection = [match]\n                        else:\n                            self.log(\n                                f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n                                \"from the knowledge base record is no longer available in the model registry. \"\n                                \"Please re-create this knowledge base with a supported embedding model.\"\n                            )\n                            msg = (\n                                f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n                                \"The knowledge base was created with an older format and the model \"\n                                \"is not available in the current registry. \"\n                                \"Please re-create the knowledge base with a supported embedding model.\"\n                            )\n                            raise ValueError(msg)\n                    except ValueError:\n                        raise\n                    except Exception:  # noqa: BLE001\n                        self.log(\n                            f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n                            \"Please re-create this knowledge base with a supported embedding model.\"\n                        )\n                        msg = (\n                            f\"Could not look up embedding model '{embedding_model_name}' \"\n                            f\"(provider: {embedding_provider}). \"\n                            \"Please re-create the knowledge base with a supported embedding model.\"\n                        )\n                        raise ValueError(msg)  # noqa: B904\n\n            if not model_selection:\n                msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n                raise ValueError(msg)\n\n            # ``api_key=None`` falls through to the user's variables table and\n            # then the environment inside ``get_embeddings`` — the same\n            # resolution the server-side ingestion path uses. The component input\n            # only overrides it.\n            embedding_function = get_embeddings(\n                model=model_selection,\n                user_id=self.user_id,\n                api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n                chunk_size=self.chunk_size,\n            )\n\n            kb_path = await self._kb_path()\n\n            run_id, run_job_id, run_summary, kb_record_id = await self._begin_ingestion_run(kb_path)\n            if kb_record_id is not None:\n                await self._record_kb_status(kb_record_id, \"ingesting\")\n\n            backend = await self._create_vector_store(\n                df_source, config_list, embedding_function=embedding_function, job_id=run_job_id\n            )\n\n            try:\n                # Stamp the KB with the file extensions we just ingested so\n                # the Knowledge Bases list renders the correct icon for\n                # flow-driven ingestion (input_df), matching direct upload.\n                # We look at the raw input first because ``convert_to_dataframe``\n                # drops Message/Data metadata fields (file_path, mimetype, …)\n                # when projecting onto the DataFrame.\n                source_types = self._extract_source_types_from_input(input_value)\n                source_types |= self._extract_source_types_from_df(df_source)\n                # Storage routing returns a guarded proxy with the backend's\n                # methods, so a concrete-class check would skip the refresh.\n                await self._refresh_kb_stats(\n                    kb_record_id=kb_record_id,\n                    backend=backend,\n                    extensions=source_types,\n                )\n            finally:\n                await _finish_despite_cancellation(backend.teardown())\n\n            meta: dict[str, Any] = {\n                \"kb_id\": str(uuid.uuid4()),\n                \"kb_name\": self.knowledge_base,\n                \"rows\": len(df_source),\n                \"column_metadata\": column_metadata,\n                \"path\": str(kb_path),\n                \"config_columns\": len(config_list),\n                \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n            }\n\n            if run_summary is not None:\n                run_summary.record_item(\n                    IngestionItemResult(\n                        item_id=self.knowledge_base,\n                        display_name=f\"{self.knowledge_base} ({len(df_source)} rows)\",\n                        status=IngestionItemStatus.SUCCEEDED,\n                        chunks_created=len(df_source),\n                    ),\n                )\n\n            if kb_record_id is not None:\n                # Stats were already refreshed straight from the backend above.\n                await self._record_kb_status(kb_record_id, \"ready\")\n\n            self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n            return Data(data=meta)\n\n        except asyncio.CancelledError:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = \"Flow ingestion was cancelled.\"\n            raise\n        except KnowledgeIngestionCancelledError as e:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = str(e)\n            raise\n        except (OSError, ValueError, RuntimeError, KeyError) as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            msg = f\"Error during KB ingestion: {e}\"\n            raise RuntimeError(msg) from e\n        except Exception as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            raise\n        finally:\n\n            async def settle_run() -> None:\n                if run_id is not None and run_summary is not None:\n                    await self._finalize_ingestion_run(\n                        run_id=run_id,\n                        job_id=run_job_id,\n                        summary=run_summary,\n                        status=run_status,\n                        error_message=run_error,\n                    )\n                if kb_record_id is not None:\n                    if run_status is IngestionRunStatus.FAILED:\n                        await self._record_kb_status(kb_record_id, \"failed\", failure_reason=run_error)\n                    elif run_status is IngestionRunStatus.CANCELLED:\n                        await self._record_kb_status(\n                            kb_record_id, previous_kb_status, failure_reason=previous_failure_reason\n                        )\n\n            # A stopped build can cancel this task more than once, and the\n            # writes that make the run terminal must survive every cancel.\n            await _finish_despite_cancellation(settle_run())\n\n    async def _begin_ingestion_run(\n        self,\n        kb_path: Path,\n    ) -> tuple[uuid.UUID | None, uuid.UUID | None, IngestionSummary | None, uuid.UUID | None]:\n        \"\"\"Create a parent ``Job`` and seed an ingestion-run row.\"\"\"\n        if not self.user_id:\n            self.log(\"No user_id on component; skipping ingestion-run tracking.\")\n            return None, None, None, None\n\n        try:\n            user_uuid = uuid.UUID(str(self.user_id))\n        except (ValueError, TypeError) as exc:\n            self.log(f\"Could not coerce user_id={self.user_id!r} to UUID; skipping run tracking ({exc}).\")\n            return None, None, None, None\n\n        try:\n            from langflow.api.utils import ingestion_run_service, knowledge_base_service\n            from langflow.services.database.models.jobs.model import JobStatus, JobType\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion will not be recorded ({exc}).\")\n            return None, None, None, None\n\n        job_id: uuid.UUID | None = None\n        run_id: uuid.UUID | None = None\n        summary: IngestionSummary | None = None\n        try:\n            kb_record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n            kb_record_id = kb_record.id if kb_record is not None else None\n\n            user_metadata = self._parse_user_metadata_dict()\n\n            job_id = uuid.uuid4()\n            job_service = get_job_service()\n            raw_flow_id = getattr(self, \"flow_id\", None)\n            flow_id_uuid = uuid.UUID(str(raw_flow_id)) if raw_flow_id else job_id\n            source = FlowComponentSource(\n                user_id=user_uuid,\n                source_config={\n                    \"knowledge_base\": self.knowledge_base,\n                    \"kb_path\": str(kb_path),\n                    \"flow_id\": str(flow_id_uuid),\n                },\n            )\n            summary = IngestionSummary(\n                kb_name=self.knowledge_base,\n                source_type=source.source_type.value,\n                user_id=user_uuid,\n                job_id=job_id,\n                source_config=source.describe().get(\"config\") or {},\n                user_metadata=user_metadata,\n            )\n            # Inserted running and already heartbeated, so an orphan sweep in\n            # another worker never sees this live run without a heartbeat.\n            await job_service.create_job(\n                job_id=job_id,\n                flow_id=flow_id_uuid,\n                job_type=JobType.INGESTION,\n                asset_id=kb_record_id,\n                asset_type=\"knowledge_base\",\n                user_id=user_uuid,\n                status=JobStatus.IN_PROGRESS,\n                heartbeat=True,\n            )\n\n            run_id = await ingestion_run_service.create_run(\n                kb_name=self.knowledge_base,\n                source=source,\n                job_id=job_id,\n                user_id=user_uuid,\n                kb_id=kb_record_id,\n                user_metadata=user_metadata,\n            )\n            if run_id is None:\n                return None, job_id, None, kb_record_id\n            await ingestion_run_service.mark_running(run_id)\n\n            self.log(f\"Started ingestion run job_id={job_id} kb_name={self.knowledge_base} kb_id={kb_record_id}\")\n            # Keeps the job heartbeated until _finalize_ingestion_run stops it.\n            await job_service.start_keep_alive(job_id)\n        except asyncio.CancelledError:\n            # The caller has not received the run ID yet, so it cannot finalize\n            # a cancellation that arrives while tracking is being initialized.\n            if job_id is not None and summary is not None:\n                await _finish_despite_cancellation(\n                    self._finalize_ingestion_run(\n                        run_id=run_id or job_id,\n                        job_id=job_id,\n                        summary=summary,\n                        status=IngestionRunStatus.CANCELLED,\n                        error_message=\"Flow ingestion was cancelled.\",\n                    )\n                )\n            raise\n        except Exception as exc:  # noqa: BLE001 — telemetry must never abort ingestion\n            self.log(f\"Could not begin ingestion-run tracking: {exc}\")\n            return None, None, None, None\n        else:\n            return run_id, job_id, summary, kb_record_id\n\n    async def _finalize_ingestion_run(\n        self,\n        *,\n        run_id: uuid.UUID,\n        job_id: uuid.UUID | None,\n        summary: IngestionSummary,\n        status: IngestionRunStatus,\n        error_message: str | None,\n    ) -> None:\n        \"\"\"Persist the final summary and transition the parent Job.\"\"\"\n        try:\n            from langflow.api.utils import ingestion_run_service\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion-run finalize skipped ({exc}).\")\n            return\n\n        try:\n            if job_id is not None:\n                # The heartbeat also rewrites job_metadata, so it stops first.\n                await get_job_service().stop_keep_alive(job_id)\n            await ingestion_run_service.finalize_run(\n                run_id,\n                summary=summary,\n                status=status,\n                error_message=error_message,\n            )\n            if job_id is not None:\n                terminal_status = {\n                    IngestionRunStatus.FAILED: JobStatus.FAILED,\n                    IngestionRunStatus.CANCELLED: JobStatus.CANCELLED,\n                }.get(status, JobStatus.COMPLETED)\n                await get_job_service().update_job_status(job_id, terminal_status, finished_timestamp=True)\n        except Exception as exc:  # noqa: BLE001 — telemetry must never re-raise\n            self.log(f\"Could not finalize ingestion-run tracking: {exc}\")\n\n    async def _record_kb_status(\n        self,\n        kb_record_id: uuid.UUID,\n        status_value: str,\n        *,\n        failure_reason: str | None = None,\n    ) -> None:\n        \"\"\"Mirror Path A's KB-row status transitions.\"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n            from langflow.services.database.models.knowledge_base.model import KnowledgeBaseStatus\n        except ImportError:\n            return\n        try:\n            await knowledge_base_service.update_status(\n                kb_record_id,\n                status=KnowledgeBaseStatus(status_value),\n                failure_reason=failure_reason,\n            )\n        except Exception as exc:  # noqa: BLE001\n            self.log(f\"Could not update KB status to {status_value}: {exc}\")\n\n    def _parse_user_metadata_dict(self) -> dict[str, Any]:\n        \"\"\"Decode ``metadata_json`` to a dict, or ``{}`` on any error.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return {}\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return {}\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError):\n            return {}\n        return decoded if isinstance(decoded, dict) else {}\n\n    # =====================================================================\n    #                       RETRIEVAL CODE PATH\n    # =====================================================================\n    @property\n    def _user_uuid(self) -> uuid.UUID | None:\n        \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n        if not self.user_id:\n            return None\n        return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n    async def _get_kb_metadata(self, knowledge_base: str | None = None) -> dict:\n        \"\"\"Load this knowledge base's embedding config from its database row.\n\n        The row is the sole authority. There is no on-disk sidecar to fall back\n        to, which is what lets retrieval work identically on a replica whose\n        filesystem never held the KB directory.\n        \"\"\"\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return {}\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return {}\n        record = await knowledge_base_service.get_by_user_and_name(\n            user_uuid,\n            knowledge_base or self.knowledge_base,\n        )\n        if record is None:\n            return {}\n        return knowledge_base_service.record_to_metadata_dict(record)\n\n    async def _backend_from_record(self) -> tuple[str, dict[str, Any]] | None:\n        \"\"\"Read ``(backend_type, backend_config)`` off the KB's database row.\n\n        Returns ``None`` when there is no row to read — a legacy KB predating the\n        record, or bare lfx with no langflow installed. A lookup that *fails* is a\n        different situation and is allowed to propagate: guessing a backend after a\n        database error is how vectors end up in a store nothing queries.\n        \"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return None\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return None\n        record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n        if record is None:\n            return None\n        return record.backend_type or BackendType.CHROMA.value, record.backend_config or {}\n\n    async def _resolve_backend_config(self) -> tuple[str, dict[str, Any]]:\n        \"\"\"Resolve this KB's backend from its database row.\n\n        Ingestion and retrieval both land here, so they cannot disagree about\n        where this KB's vectors live. A backend that cannot be resolved raises\n        rather than defaulting to local storage: guessing is how a pod ends up\n        writing vectors to a local directory while queries follow the real config\n        to a remote store and silently find nothing.\n        \"\"\"\n        resolved = await self._backend_from_record()\n        if resolved is None:\n            msg = (\n                f\"Cannot determine the vector-store backend for knowledge base \"\n                f\"'{self.knowledge_base}': it has no database record. Refusing to fall \"\n                f\"back to local storage, which would write to a different store than \"\n                f\"queries read from. If this knowledge base predates database-backed \"\n                f\"metadata, adopt it with 'langflow reconcile-kb-from-disk'.\"\n            )\n            raise ValueError(msg)\n        return resolved\n\n    def _resolve_model_selection(self, metadata: dict[str, Any]) -> list[dict[str, Any]]:\n        \"\"\"Resolve the ``get_embeddings``-compatible model selection from metadata.\"\"\"\n        model_selection = metadata.get(\"model_selection\")\n        if model_selection:\n            selection_list = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            return [self._hydrate_model_metadata(entry) for entry in selection_list]\n\n        embedding_model_name = metadata.get(\"embedding_model\")\n        embedding_provider = metadata.get(\"embedding_provider\", \"Unknown\")\n        if not embedding_model_name:\n            msg = (\n                f\"Knowledge base '{self.knowledge_base}' has no embedding model recorded; \"\n                \"re-create it with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n\n        match = self._find_catalog_entry(embedding_model_name)\n        if match is None:\n            msg = (\n                f\"Embedding model '{embedding_model_name}' (provider '{embedding_provider}') \"\n                \"recorded for this knowledge base is no longer available in the model registry. \"\n                \"Please re-create the knowledge base with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n        return [match]\n\n    def _hydrate_model_metadata(self, entry: dict[str, Any]) -> dict[str, Any]:\n        \"\"\"Fill in ``metadata.embedding_class`` / ``param_mapping`` if missing.\"\"\"\n        entry_metadata = entry.get(\"metadata\") or {}\n        has_class = bool(entry_metadata.get(\"embedding_class\"))\n        has_mapping = bool(entry_metadata.get(\"param_mapping\"))\n        if has_class and has_mapping:\n            return entry\n\n        model_name = entry.get(\"name\")\n        if not model_name:\n            return entry\n\n        catalog_entry = self._find_catalog_entry(model_name)\n        if catalog_entry is None:\n            return entry\n\n        catalog_metadata = catalog_entry.get(\"metadata\") or {}\n        merged_metadata = {**catalog_metadata, **entry_metadata}\n        return {**entry, \"metadata\": merged_metadata}\n\n    def _find_catalog_entry(self, model_name: str) -> dict[str, Any] | None:\n        \"\"\"Look up an embedding model by name in the unified-models catalog.\"\"\"\n        options = get_embedding_model_options(user_id=self.user_id)\n        return next((o for o in options if o.get(\"name\") == model_name), None)\n\n    async def retrieve_data(self) -> DataFrame:\n        \"\"\"Retrieve data from the selected knowledge base.\n\n        Annotation narrowed to ``DataFrame`` to keep this output's handle\n        type list at ``[\"Table\"]`` only; widening to a union surfaces\n        ``[\"Table\", \"JSON\"]`` on the API and breaks every starter-project\n        edge whose sourceHandle was stored against a single-type handle\n        (BUG-02). The cross-mode defensive fallback may still return a\n        ``Data`` at runtime — Python ignores return annotations, so\n        nothing breaks.\n        \"\"\"\n        if not _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.build_kb_info()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        if not self.user_id:\n            msg = \"User ID is required for fetching Knowledge Base data.\"\n            raise ValueError(msg)\n\n        # Saved flows can load in standalone lfx, but knowledge retrieval\n        # requires the application's user database and storage services.\n        try:\n            from langflow.services.database.models.user.crud import get_user_by_id\n        except ModuleNotFoundError as exc:\n            if exc.name != \"langflow\":\n                raise\n            msg = \"Knowledge Base retrieval requires OpenXFlow's application services. Run this flow in OpenXFlow.\"\n            raise RuntimeError(msg) from None\n\n        async with session_scope() as db:\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n\n        metadata = await self._get_kb_metadata()\n        if not metadata:\n            msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n            raise ValueError(msg)\n\n        model_selection = self._resolve_model_selection(metadata)\n        chunk_size = metadata.get(\"chunk_size\")\n\n        # Resolve where this KB lives before instantiating embeddings: path\n        # containment is a cheap local check, and a request that will be refused\n        # shouldn't first pay for a credential lookup and a provider client.\n        backend_type, _backend_config = await self._resolve_backend_config()\n\n        # ``api_key=None`` is not \"no credential\": ``get_embeddings`` resolves the\n        # provider's key from the user's variables table and then the environment,\n        # exactly as the server-side ingestion path does. The component input just\n        # takes precedence when supplied.\n        embedding_function = get_embeddings(\n            model=model_selection,\n            user_id=self.user_id,\n            api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n            chunk_size=chunk_size,\n        )\n\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            user_metadata_filter = _parse_metadata_filter(getattr(self, \"metadata_filter\", None))\n            use_scores = bool(self.search_query)\n            exact_filter = backend_type == BackendType.SQLITE.value\n            search_k = self.top_k if exact_filter or not user_metadata_filter else self.top_k * 4\n            search_options = {\"source_filter\": user_metadata_filter} if exact_filter else {}\n            results = await backend.similarity_search(\n                query=self.search_query or \"\",\n                k=search_k,\n                with_scores=use_scores,\n                **search_options,\n            )\n            if user_metadata_filter and not exact_filter:\n                results = [\n                    (doc, score) for doc, score in results if _chunk_matches_filter(doc.metadata, user_metadata_filter)\n                ][: self.top_k]\n\n            embeddings_by_key: dict[tuple[str, str], list[float]] = {}\n            if self.include_embeddings and results:\n                # Join each retrieved chunk to its stored embedding. We key on\n                # ``_id`` when present and fall back to page content otherwise,\n                # so KBs populated by direct file upload — whose chunks carry no\n                # ``_id`` — still resolve. See ``_embedding_match_key``.\n                wanted_keys = {_embedding_match_key(doc.page_content, doc.metadata) for doc, _score in results}\n                async with aclosing(backend.iter_documents(include_embeddings=True)) as batches:\n                    async for batch in batches:\n                        for entry in batch:\n                            if entry.embedding is None:\n                                continue\n                            key = _embedding_match_key(entry.content, entry.metadata)\n                            if key in wanted_keys:\n                                embeddings_by_key[key] = entry.embedding\n                        if wanted_keys <= embeddings_by_key.keys():\n                            break\n\n            data_list: list[Data] = []\n            for doc, score in results:\n                kwargs: dict[str, Any] = {\"content\": doc.page_content}\n                if use_scores:\n                    kwargs[\"_score\"] = backend.normalize_score(score)\n                if self.include_metadata:\n                    kwargs.update(doc.metadata)\n                if self.include_embeddings:\n                    kwargs[\"_embeddings\"] = embeddings_by_key.get(_embedding_match_key(doc.page_content, doc.metadata))\n                data_list.append(Data(**kwargs))\n\n            return DataFrame(data=data_list)\n        finally:\n            await backend.teardown()\n\n\ndef _embedding_match_key(content: str, metadata: dict[str, Any] | None) -> tuple[str, str]:\n    \"\"\"Build a stable key for aligning a retrieved chunk with its stored embedding.\n\n    Embeddings are gathered separately (via ``iter_documents``) and then joined\n    back onto the search results. Component-driven ingestion stamps a\n    content-hash ``_id`` on every chunk, but direct file-upload ingestion\n    (``KBIngestionHelper.perform_ingestion``) does not. Keying the join purely on\n    ``_id`` therefore left every upload-populated KB with ``_embeddings: None``.\n\n    We prefer ``_id`` when present (so legitimately distinct chunks that happen to\n    share text aren't collapsed) and fall back to the chunk's page content\n    otherwise. The content fallback is exact for the embedding use case: two\n    chunks with identical text necessarily share the same embedding vector. The\n    leading ``\"id\"`` / ``\"content\"`` tag namespaces the two key spaces so a mixed\n    KB (some chunks with ``_id``, some without) never cross-matches.\n    \"\"\"\n    doc_id = metadata.get(\"_id\") if metadata else None\n    if doc_id:\n        return (\"id\", str(doc_id))\n    return (\"content\", content or \"\")\n\n\ndef _parse_metadata_filter(raw: str | None) -> dict[str, list[str]]:\n    \"\"\"Decode the ``metadata_filter`` input into a {key: [values]} map.\n\n    Empty or malformed input maps to an empty filter so retrieval falls back\n    to the unfiltered path. JSON errors are swallowed here rather than raised:\n    surfacing component-config errors at the canvas node would break a flow\n    run for what is meant to be an optional refinement.\n    \"\"\"\n    if not raw:\n        return {}\n    text = raw.strip() if isinstance(raw, str) else raw\n    if not text:\n        return {}\n    try:\n        decoded = json.loads(text)\n    except (TypeError, json.JSONDecodeError):\n        logger.warning(\"KnowledgeComponent: metadata_filter is not valid JSON; ignoring filter.\")\n        return {}\n    if not isinstance(decoded, dict):\n        logger.warning(\"KnowledgeComponent: metadata_filter must be a JSON object; ignoring filter.\")\n        return {}\n    result: dict[str, list[str]] = {}\n    for key, value in decoded.items():\n        if not isinstance(key, str):\n            continue\n        if isinstance(value, list):\n            result[key] = [str(entry) for entry in value]\n        else:\n            result[key] = [str(value)]\n    return result\n\n\ndef _chunk_matches_filter(metadata: dict[str, Any] | None, filt: dict[str, list[str]]) -> bool:\n    \"\"\"AND across keys, OR within key values, mirroring the chunks endpoint.\"\"\"\n    if not filt:\n        return True\n    if not metadata:\n        return False\n    raw = metadata.get(\"source_metadata\")\n    if not raw:\n        return False\n    try:\n        stored = json.loads(raw) if isinstance(raw, str) else raw\n    except json.JSONDecodeError:\n        return False\n    if not isinstance(stored, dict):\n        return False\n    for key, expected_values in filt.items():\n        actual = stored.get(key)\n        if actual is None:\n            return False\n        actual_set = {str(entry) for entry in actual} if isinstance(actual, list) else {str(actual)}\n        if not actual_set & set(expected_values):\n            return False\n    return True\n"
+                "value": "\"\"\"Unified Knowledge component — ingest into or retrieve from a knowledge base.\n\nThis component merges what used to live in ``ingestion.py`` and\n``retrieval.py`` into a single, mode-driven component. A ``TabInput``\n(\"📥 Ingest\" / \"🔍 Retrieve\") drives which inputs and which output are\nvisible. The merged shape gives users one node per KB instead of two\nparallel ones that always shared the same ``knowledge_base`` picker,\nembedding-model metadata, and backend registry.\n\nBoth legacy classes (``KnowledgeIngestionComponent``,\n``KnowledgeBaseComponent``) remain importable as thin subclasses of\n``KnowledgeComponent`` — see the sibling ``ingestion.py`` / ``retrieval.py``\nmodules — so saved flows continue to load unchanged.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport uuid\nfrom collections.abc import Awaitable, Mapping\nfrom contextlib import aclosing\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, TypeVar\n\nimport anyio\nimport pandas as pd\n\nfrom lfx.base.knowledge_bases.backends import BackendType, BaseVectorStoreBackend\nfrom lfx.base.knowledge_bases.backends.base import BackendConfigurationError, IngestedDocument\nfrom lfx.base.knowledge_bases.backends.naming import ensure_storage_routing_allowed\nfrom lfx.base.knowledge_bases.ingestion_sources.base import (\n    IngestionItemResult,\n    IngestionItemStatus,\n    IngestionRunStatus,\n    IngestionSummary,\n)\nfrom lfx.base.knowledge_bases.ingestion_sources.flow_component import FlowComponentSource\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.knowledge_bases.validation import (\n    is_valid_collection_name as is_valid_kb_collection_name,\n)\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.helpers.base_model import coalesce_bool\nfrom lfx.io import (\n    BoolInput,\n    DBProviderInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MessageTextInput,\n    ModelInput,\n    Output,\n    SecretStrInput,\n    StrInput,\n    TabInput,\n    TableInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n    session_scope,\n)\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n\ndef _inputs_for_mode(default_mode: str) -> list:\n    \"\"\"Return a fresh copy of the canonical inputs list with show flags set for the given mode.\n\n    Used by the legacy subclasses so a saved flow keyed on\n    ``KnowledgeIngestion`` / ``KnowledgeBase`` lands on a node template\n    whose default visibility already matches the pinned mode — no\n    \"wait for the user to click the mode tab\" UX gap on load.\n    \"\"\"\n    always_visible = set(KnowledgeComponent.default_keys) | set(KnowledgeComponent.mode_config[default_mode])\n    inputs_copy = []\n    for inp in KnowledgeComponent.inputs:\n        clone = inp.model_copy(deep=True) if hasattr(inp, \"model_copy\") else inp\n        if clone.name == \"mode\":\n            clone.value = default_mode\n        else:\n            clone.show = clone.name in always_visible\n        inputs_copy.append(clone)\n    return inputs_copy\n\n\nif TYPE_CHECKING:\n    from pathlib import Path\n\n# Mode constants. Plain-text labels (no emoji) for consistency with the rest of\n# Langflow's TabInput palette — see ``MemoryComponent`` for the same convention.\nMODE_INGEST = \"Ingest\"\nMODE_RETRIEVE = \"Retrieve\"\n\n# How often a flow ingestion checks whether its job was cancelled from the\n# Knowledge page while it waits on the embedding provider.\nJOB_CANCEL_POLL_SECONDS = 1.0\n\n\nclass KnowledgeIngestionCancelledError(Exception):\n    \"\"\"The ingestion's job was cancelled from the Knowledge page.\"\"\"\n\n\n_T = TypeVar(\"_T\")\n\n\nasync def _finish_despite_cancellation(awaitable: Awaitable[_T]) -> _T:\n    \"\"\"Run ``awaitable`` to completion even if the caller is cancelled meanwhile.\n\n    ``anyio.CancelScope(shield=True)`` holds off AnyIO cancellation, but a\n    repeated native ``Task.cancel()`` still interrupts it, and a stopped flow\n    build can cancel its task more than once. The work runs in its own task,\n    and the caller waits through every cancellation, re-raising once the work\n    has landed.\n    \"\"\"\n    task = asyncio.ensure_future(awaitable)\n    cancelled = False\n    with anyio.CancelScope(shield=True):\n        while not task.done():\n            try:\n                await asyncio.wait({task})\n            except asyncio.CancelledError:\n                cancelled = True\n    if cancelled:\n        error = None if task.cancelled() else task.exception()\n        if error is not None:\n            logger.warning(f\"Work finished during cancellation failed: {error!r}\")\n        raise asyncio.CancelledError from error\n    return task.result()\n\n\ndef _is_retrieve_mode(value: Any) -> bool:\n    \"\"\"Lenient mode check: treats any label containing 'Retrieve' as retrieve mode.\n\n    Older saved flows may carry the emoji-prefixed labels (\"📥 Ingest\" /\n    \"🔍 Retrieve\") this component used to ship with; substring matching\n    keeps those loading without forcing a flow rewrite.\n    \"\"\"\n    return isinstance(value, str) and \"Retrieve\" in value\n\n\n# Boolean flags of a ``column_config`` row. Toggled cells hold real booleans,\n# but a typed cell (or a flow saved while the cell rendered as plain text)\n# keeps the raw string, e.g. ``\"true\"`` — read them with ``coalesce_bool``.\n_COLUMN_FLAGS = (\"vectorize\", \"identifier\")\n\n\ndef _normalize_column_config(config_list: list[dict[str, Any]]) -> list[dict[str, Any]]:\n    \"\"\"Return copies of the rows with every flag as a real boolean.\"\"\"\n    return [{**row, **{key: coalesce_bool(row.get(key)) for key in _COLUMN_FLAGS}} for row in config_list]\n\n\n# Error message used by both the ingest and retrieve paths when the user is\n# running against an Astra cloud environment that disables these flows.\nastra_error_msg = \"Knowledge ingestion and retrieval are not supported in Astra cloud environment.\"\n\n_DEFAULT_OPENSEARCH_CONFIG = {\n    \"url_variable\": \"OPENSEARCH_URL\",\n    \"username_variable\": \"OPENSEARCH_USERNAME\",\n    \"password_variable\": \"OPENSEARCH_PASSWORD\",  # pragma: allowlist secret\n    \"index_name\": \"\",\n    \"vector_field\": \"vector_field\",\n    \"text_field\": \"text\",\n}\n\n\nclass KnowledgeComponent(Component):\n    \"\"\"One component for both writing into and reading from an OpenXFlow knowledge base.\n\n    A ``TabInput`` switches between ingestion and retrieval. The\n    ``update_build_config`` / ``update_outputs`` hooks hide the inputs\n    and the output that don't apply to the current mode so the canvas\n    node stays focused.\n    \"\"\"\n\n    display_name = \"Knowledge\"\n    description = \"Ingest into or retrieve from an OpenXFlow knowledge base.\"\n    icon = \"database\"\n    name = \"Knowledge\"\n\n    # ------ Mode → visible-fields wiring ---------------------------------\n    # ``default_keys`` are inputs always visible regardless of mode.\n    # ``mode_config`` lists the inputs unique to each mode; everything outside\n    # this set is hidden when its mode is not selected.\n    default_keys: list[str] = [\"mode\", \"knowledge_base\"]\n    mode_config: dict[str, list[str]] = {\n        MODE_INGEST: [\n            \"input_df\",\n            \"column_config\",\n            \"chunk_size\",\n            \"api_key\",\n            \"allow_duplicates\",\n            \"metadata_json\",\n        ],\n        MODE_RETRIEVE: [\n            \"search_query\",\n            \"top_k\",\n            \"include_metadata\",\n            \"include_embeddings\",\n            \"metadata_filter\",\n        ],\n    }\n\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self._cached_kb_path: Path | None = None\n        self._knowledge_output_result: Data | DataFrame | None = None\n\n    def _pre_run_setup(self) -> None:\n        self._knowledge_output_result = None\n\n    async def _get_output_result(self, output: Output):\n        # Both legacy handles dispatch to the selected mode. Terminal nodes\n        # evaluate both handles, but must execute the storage operation once.\n        if output.method not in {\"build_kb_info\", \"retrieve_data\"}:\n            return await super()._get_output_result(output)\n        if self._knowledge_output_result is None:\n            raw_output = output.model_copy(update={\"options\": None})\n            self._knowledge_output_result = await super()._get_output_result(raw_output)\n        output.value = output.apply_options(self._knowledge_output_result)\n        return output.value\n\n    @staticmethod\n    def _embedding_provider_from_selection(selection: Any) -> str | None:\n        \"\"\"Read a provider name from a raw or persisted model selection.\"\"\"\n        if isinstance(selection, list):\n            selection = selection[0] if selection else None\n        if not isinstance(selection, Mapping):\n            return None\n        provider = selection.get(\"provider\")\n        if not isinstance(provider, str) or not provider.strip():\n            return None\n        return provider.strip()\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Resolve the embedding provider before dialog hooks or runtime secrets.\n\n        New-KB configuration carries its ModelInput inside a dropdown dialog,\n        while existing KBs persist the provider in the KB row. Neither is a\n        top-level ModelInput, so both must be resolved explicitly before the\n        generic component pipeline hydrates ``api_key``.\n        \"\"\"\n        from lfx.base.models.provider_registry import resolve_provider_id\n        from lfx.services.model_provider_policy import ModelProviderPolicyError\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        knowledge_value = effective_parameters.get(\"knowledge_base\", getattr(self, \"knowledge_base\", None))\n\n        if isinstance(knowledge_value, Mapping):\n            if \"02_embedding_model\" not in knowledge_value:\n                return ()\n            provider = self._embedding_provider_from_selection(knowledge_value.get(\"02_embedding_model\"))\n        elif isinstance(knowledge_value, str) and knowledge_value.strip():\n            metadata = await self._get_kb_metadata(knowledge_value.strip())\n            provider = self._embedding_provider_from_selection(metadata.get(\"model_selection\"))\n            if provider is None:\n                legacy_provider = metadata.get(\"embedding_provider\")\n                provider = legacy_provider.strip() if isinstance(legacy_provider, str) else None\n                if provider == \"Unknown\":\n                    provider = None\n        else:\n            return ()\n\n        if provider is None:\n            unknown_provider = \"unknown\"\n            raise ModelProviderPolicyError(unknown_provider, purpose)\n        return (resolve_provider_id(provider),)\n\n    @dataclass\n    class NewKnowledgeBaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"name\": \"create_knowledge_base\",\n                        \"description\": \"Create new knowledge in OpenXFlow.\",\n                        \"display_name\": \"Create new Knowledge Base\",\n                        \"field_order\": [\n                            \"01_new_kb_name\",\n                            \"02_embedding_model\",\n                            \"03_knowledge_backend\",\n                        ],\n                        \"template\": {\n                            \"01_new_kb_name\": StrInput(\n                                name=\"new_kb_name\",\n                                display_name=\"Knowledge Name\",\n                                info=\"Name of the new knowledge to create.\",\n                                required=True,\n                            ),\n                            \"02_embedding_model\": ModelInput(\n                                name=\"embedding_model\",\n                                display_name=\"Choose Embedding Model\",\n                                info=(\n                                    \"Select the embedding model to use for this knowledge base. \"\n                                    \"OpenXFlow uses the configured credentials for that model provider.\"\n                                ),\n                                required=True,\n                                model_type=\"embedding\",\n                            ),\n                            \"03_knowledge_backend\": DBProviderInput(\n                                name=\"knowledge_backend\",\n                                display_name=\"DB Provider\",\n                                info=(\n                                    \"Select where this knowledge base stores vectors. \"\n                                    \"OpenSearch uses the global DB Providers settings.\"\n                                ),\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    # ------ Inputs --------------------------------------------------------\n    # ``knowledge_base`` is kept at position 0 so legacy tests that check\n    # ``component.inputs[0].dialog_inputs`` continue to work.\n    inputs = [\n        DropdownInput(\n            name=\"knowledge_base\",\n            display_name=\"Knowledge\",\n            info=\"Select the knowledge to load data from.\",\n            required=True,\n            options=[],\n            refresh_button=True,\n            real_time_refresh=True,\n            dialog_inputs=asdict(NewKnowledgeBaseInput()),\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[MODE_INGEST, MODE_RETRIEVE],\n            value=MODE_INGEST,\n            info=\"Switch between writing new data into the knowledge base and querying it.\",\n            real_time_refresh=True,\n            tool_mode=True,\n        ),\n        # --- Ingest-only inputs (default-shown; hidden when mode == Retrieve) -\n        HandleInput(\n            name=\"input_df\",\n            display_name=\"Input\",\n            info=(\n                \"Table with all original columns (already chunked / processed). \"\n                \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n                \"it is converted to a DataFrame automatically.\"\n            ),\n            input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n            required=True,\n            dynamic=True,\n            show=True,\n        ),\n        TableInput(\n            name=\"column_config\",\n            display_name=\"Column Configuration\",\n            info=\"Configure column behavior for the knowledge base.\",\n            required=True,\n            table_schema=[\n                {\n                    \"name\": \"column_name\",\n                    \"display_name\": \"Column Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Name of the column in the source DataFrame\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"vectorize\",\n                    \"display_name\": \"Vectorize\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Create embeddings for this column\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"identifier\",\n                    \"display_name\": \"Identifier\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Use this column as unique identifier\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"column_name\": \"text\",\n                    \"vectorize\": True,\n                    \"identifier\": True,\n                },\n            ],\n            dynamic=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"Batch size for processing embeddings\",\n            advanced=True,\n            value=1000,\n            dynamic=True,\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Embedding Provider API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        BoolInput(\n            name=\"allow_duplicates\",\n            display_name=\"Allow Duplicates\",\n            info=\"Allow duplicate rows in the knowledge base\",\n            advanced=True,\n            value=False,\n            dynamic=True,\n            show=True,\n        ),\n        StrInput(\n            name=\"metadata_json\",\n            display_name=\"Metadata\",\n            info=(\n                \"Optional JSON object of user metadata applied to every chunk produced by this \"\n                'run (e.g. {\"tag\": \"invoice\", \"year\": \"2026\"}). Same shape as the upload modal '\n                \"Metadata section so chunks browser filters + Knowledge retrieval metadata_filter \"\n                \"work uniformly across upload, folder, and flow-driven ingestion. Malformed JSON is \"\n                \"ignored with a warning rather than failing the run.\"\n            ),\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        # --- Retrieve-only inputs (default-hidden; shown when mode == Retrieve) -\n        MessageTextInput(\n            name=\"search_query\",\n            display_name=\"Search Query\",\n            info=\"Optional search query to filter knowledge base data.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of top results to return from the knowledge base.\",\n            value=5,\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_metadata\",\n            display_name=\"Include Metadata\",\n            info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n            value=True,\n            advanced=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_embeddings\",\n            display_name=\"Include Embeddings\",\n            info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n            value=False,\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"metadata_filter\",\n            display_name=\"Metadata Filter\",\n            info=(\n                \"Optional JSON object of user-metadata key/value pairs. Only chunks \"\n                'whose source_metadata matches every key are returned (e.g. {\"tag\": \"invoice\"} '\n                'or {\"tag\": [\"invoice\", \"audit\"]} for OR-of-values). Backends without '\n                \"native filtering apply the match client-side after retrieval.\"\n            ),\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n    ]\n\n    # ------ Outputs -------------------------------------------------------\n    # Both outputs are declared at the class level so the runtime can\n    # dispatch to either method depending on which one the saved flow has\n    # wired up. ``update_outputs`` filters the canvas-visible output per\n    # selected mode; see ``TypeConverterComponent`` and ``MemoryComponent``\n    # for the same pattern.\n    #\n    # Output names match the legacy ``KnowledgeIngestionComponent``\n    # (``dataframe_output``) and ``KnowledgeBaseComponent``\n    # (``retrieve_data``) so saved flow edges keyed on those names resolve\n    # cleanly against the merged component.\n    outputs = [\n        Output(\n            display_name=\"Results\",\n            name=\"dataframe_output\",\n            method=\"build_kb_info\",\n            types=[\"JSON\"],\n            selected=\"JSON\",\n        ),\n        Output(\n            display_name=\"Results\",\n            name=\"retrieve_data\",\n            method=\"retrieve_data\",\n            info=\"Returns the data from the selected knowledge base.\",\n            types=[\"Table\"],\n            selected=\"Table\",\n        ),\n    ]\n\n    # ------ Mode-driven UI updates ---------------------------------------\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"Sync the visible output with the current ``mode`` value on canvas load.\n\n        Saved flows hit this path: ``update_outputs`` is normally only triggered\n        by ``real_time_refresh`` field edits, so without this re-sync the\n        canvas could land with both outputs visible.\n        \"\"\"\n        await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = new_frontend_node.get(\"template\", {})\n        mode_value = template.get(\"mode\", {}).get(\"value\", MODE_INGEST)\n        self.update_outputs(new_frontend_node, \"mode\", mode_value)\n        # The rebuilt template carries class-default (Ingest) ``show`` flags,\n        # so a saved Retrieve node returned ``search_query`` hidden and the canvas\n        # silently dropped the edge feeding it.\n        self._apply_mode_visibility(template)\n        return new_frontend_node\n\n    def _apply_mode_visibility(self, build_config, field_name: str | None = None, field_value: Any = None):\n        \"\"\"Show only the inputs that belong to the effective mode.\"\"\"\n        current_mode = build_config.get(\"mode\", {}).get(\"value\") if isinstance(build_config, dict) else None\n        if field_name == \"mode\":\n            current_mode = field_value\n        # Map legacy/emoji-prefixed labels onto the current canonical values so\n        # flows saved before the label change still toggle visibility correctly.\n        if _is_retrieve_mode(current_mode):\n            current_mode = MODE_RETRIEVE\n        elif current_mode not in self.mode_config:\n            current_mode = MODE_INGEST\n        return set_current_fields(\n            build_config=build_config if isinstance(build_config, dotdict) else dotdict(build_config),\n            action_fields=self.mode_config,\n            selected_action=current_mode,\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Filter visible outputs to match the selected mode.\n\n        Triggered by the ``mode`` ``TabInput`` ``real_time_refresh`` flag.\n        The class-level ``outputs`` list always carries both entries so the\n        runtime can resolve either ``build_kb_info`` or ``retrieve_data``\n        regardless of canvas visibility — we just hide the unused one here.\n        \"\"\"\n        if field_name != \"mode\":\n            return frontend_node\n        if _is_retrieve_mode(field_value):\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"retrieve_data\",\n                    method=\"retrieve_data\",\n                    info=\"Returns the data from the selected knowledge base.\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                )\n            ]\n        else:\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"dataframe_output\",\n                    method=\"build_kb_info\",\n                    types=[\"JSON\"],\n                    selected=\"JSON\",\n                )\n            ]\n        return frontend_node\n\n    async def update_build_config(\n        self,\n        build_config,\n        field_value: Any,\n        field_name: str | None = None,\n    ):\n        \"\"\"Refresh KB options, drive the create-KB dialog, and hide off-mode fields.\"\"\"\n        # Astra-cloud gate covers both ingest and retrieve paths.\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        # Always populate the create-KB dialog's embedding-model options so the\n        # ModelInput renders correctly regardless of which input triggered the\n        # refresh.\n        try:\n            dialog_template = (\n                build_config[\"knowledge_base\"]\n                .get(\"dialog_inputs\", {})\n                .get(\"fields\", {})\n                .get(\"data\", {})\n                .get(\"node\", {})\n                .get(\"template\", {})\n            )\n            if \"02_embedding_model\" in dialog_template:\n                embedding_options = get_embedding_model_options(user_id=self.user_id)\n                dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n        except Exception:  # noqa: BLE001\n            self.log(\"Failed to populate embedding model options in dialog\")\n\n        # KB-picker refresh + create-new-KB flow (lifted from the legacy ingestion\n        # component verbatim; relied on by both the canvas refresh button and the\n        # dialog-submit path).\n        if field_name == \"knowledge_base\":\n            # Lazy import keeps lfx importable without langflow installed.\n            from langflow.services.database.models.user.crud import get_user_by_id\n\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for fetching knowledge base list.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n                if not current_user:\n                    msg = f\"User with ID {self.user_id} not found.\"\n                    raise ValueError(msg)\n            if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n                model_selection = field_value[\"02_embedding_model\"]\n                if isinstance(model_selection, dict):\n                    model_selection = [model_selection]\n\n                backend_type, backend_config = self._normalize_backend_selection(\n                    field_value.get(\"03_knowledge_backend\")\n                )\n                ensure_storage_routing_allowed(backend_config, is_superuser=bool(current_user.is_superuser))\n                new_kb_name = field_value[\"01_new_kb_name\"]\n                from langflow.api.utils.kb_helpers import local_chroma_rejection_reason, validate_kb_name\n\n                validate_kb_name(new_kb_name)\n                rejection = local_chroma_rejection_reason(backend_type, backend_config)\n                if rejection:\n                    raise ValueError(rejection)\n\n                embed_model = get_embeddings(\n                    model=model_selection,\n                    user_id=self.user_id,\n                )\n\n                try:\n                    await asyncio.wait_for(\n                        asyncio.to_thread(embed_model.embed_query, \"test\"),\n                        timeout=10,\n                    )\n                except TimeoutError as e:\n                    msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n                    raise ValueError(msg) from e\n                except Exception as e:\n                    msg = f\"Embedding validation failed: {e!s}\"\n                    raise ValueError(msg) from e\n\n                build_config[\"knowledge_base\"][\"value\"] = new_kb_name\n                await self._create_knowledge_base_record(\n                    user_id=self.user_id,\n                    name=field_value[\"01_new_kb_name\"],\n                    model_selection=model_selection,\n                    backend_type=backend_type,\n                    backend_config=backend_config,\n                )\n\n            build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(user_id=self.user_id)\n            if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n                build_config[\"knowledge_base\"][\"value\"] = None\n\n        # Honor the current mode regardless of which field triggered the refresh.\n        return self._apply_mode_visibility(build_config, field_name, field_value)\n\n    # =====================================================================\n    #                       INGESTION CODE PATH\n    # =====================================================================\n    @staticmethod\n    def _scalar_notna(value) -> bool:\n        \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n        ``pd.notna`` returns an array when given an array-like input, which\n        cannot be used directly in a boolean context.  This helper collapses\n        the result to a single scalar ``bool``.\n        \"\"\"\n        result = pd.notna(value)\n        if hasattr(result, \"__iter__\") and not isinstance(result, str):\n            import numpy as np\n\n            arr = np.asarray(result)\n            return arr.size > 0 and arr.all()\n        return bool(result)\n\n    def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n        \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n        if not self.column_config:\n            msg = \"Column configuration cannot be empty\"\n            raise ValueError(msg)\n\n        config_list = self.column_config if isinstance(self.column_config, list) else []\n\n        df_columns = set(df_source.columns)\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            if col_name not in df_columns:\n                msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n                raise ValueError(msg)\n\n        return config_list\n\n    @staticmethod\n    def _extract_source_types_from_df(df_source: pd.DataFrame) -> set[str]:\n        \"\"\"Pull file extensions out of common path/name columns on the source DataFrame.\n\n        The direct-upload ingestion path stores extensions in\n        the KB row's ``source_types`` so the KB list can render the\n        correct file-type icon. When ingestion happens via a connected\n        ``input_df`` (e.g. File → Knowledge) the same field stayed empty and\n        the icon defaulted to a blank tile. We look at the well-known columns\n        the File / S3 / cloud-storage components produce and collect any\n        plausible extension so the icon is consistent across both flows.\n        \"\"\"\n        candidate_columns = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for col in candidate_columns:\n            if col not in df_source.columns:\n                continue\n            for value in df_source[col].dropna():\n                ext = KnowledgeComponent._extension_from_value(value)\n                # Drop anything that doesn't look like an extension (URL\n                # query strings, version segments, etc.) — the icon palette\n                # keys off short alphanumeric tokens like \"pdf\"/\"docx\".\n                if ext:\n                    extensions.add(ext)\n        return extensions\n\n    @staticmethod\n    def _extension_from_value(value: Any) -> str | None:\n        \"\"\"Return a normalized file-extension token from a path / filename / MIME string.\n\n        Accepts values like ``\"report.PDF\"``, ``\"/docs/notes.txt\"``, or\n        ``\"application/pdf\"`` and returns ``\"pdf\"`` / ``\"txt\"``. Returns\n        ``None`` if no plausible extension can be derived.\n        \"\"\"\n        extension_length_limit = 10\n        if value is None:\n            return None\n        text = str(value).strip()\n        if not text:\n            return None\n        # MIME types like ``application/pdf`` carry the canonical extension\n        # in the subtype slot — preserve them so File / S3 messages keyed\n        # only on ``mimetype`` still resolve to an icon.\n        if \"/\" in text and \".\" not in text.rsplit(\"/\", 1)[-1]:\n            subtype = text.rsplit(\"/\", 1)[-1].strip().lower()\n            return subtype if subtype and len(subtype) <= extension_length_limit and subtype.isalnum() else None\n        if \".\" not in text:\n            return None\n        ext = text.rsplit(\".\", 1)[-1].strip().lower()\n        if ext and len(ext) <= extension_length_limit and ext.isalnum():\n            return ext\n        return None\n\n    @classmethod\n    def _extract_source_types_from_mapping(cls, mapping: Any) -> set[str]:\n        \"\"\"Pull extensions from a Message/Data-style ``data`` dict or a plain dict.\"\"\"\n        if not isinstance(mapping, dict):\n            return set()\n        candidate_keys = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for key in candidate_keys:\n            ext = cls._extension_from_value(mapping.get(key))\n            if ext:\n                extensions.add(ext)\n        return extensions\n\n    @classmethod\n    def _extract_source_types_from_input(cls, input_value: Any) -> set[str]:\n        \"\"\"Pull file extensions out of the raw component input.\n\n        ``convert_to_dataframe`` strips Message/Data fields down to ``text``\n        when projecting onto a DataFrame, so file metadata attached to a\n        File-component \"Raw Content\" output never reaches\n        ``_extract_source_types_from_df``. Looking at the raw input first\n        keeps the KB icon consistent with direct upload.\n        \"\"\"\n        if input_value is None:\n            return set()\n        if isinstance(input_value, list):\n            extensions: set[str] = set()\n            for item in input_value:\n                extensions |= cls._extract_source_types_from_input(item)\n            return extensions\n        if isinstance(input_value, pd.DataFrame):\n            return cls._extract_source_types_from_df(input_value)\n        # Message / Data / JSON all expose a ``data`` dict via the lfx schema.\n        mapping = getattr(input_value, \"data\", None)\n        if mapping is None and isinstance(input_value, dict):\n            mapping = input_value\n        return cls._extract_source_types_from_mapping(mapping)\n\n    async def _refresh_kb_stats(\n        self,\n        *,\n        kb_record_id: Any,\n        backend: BaseVectorStoreBackend,\n        extensions: set[str],\n    ) -> None:\n        \"\"\"Recount the KB from its vector store and write the totals onto the row.\n\n        Backend-agnostic: counts come from ``count`` / ``iter_documents`` /\n        ``storage_size_bytes``, so a Chroma, OpenSearch, or pgVector KB all report\n        real numbers. ``extensions`` are merged into ``source_types`` so the KB\n        list renders the right file-type icon regardless of which ingestion route\n        produced the chunks.\n\n        Best-effort: the chunks are already written and committed by the time this\n        runs, so a stats refresh failure must not fail the ingestion.\n        \"\"\"\n        if kb_record_id is None:\n            return\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return\n\n        try:\n            chunks = await backend.count()\n            characters = 0\n            words = 0\n            async with aclosing(backend.iter_documents()) as batches:\n                async for batch in batches:\n                    for document in batch:\n                        characters += len(document.content)\n                        words += len(document.content.split())\n\n            record = await knowledge_base_service.get_by_id(kb_record_id)\n            existing = set(record.source_types or []) if record is not None else set()\n\n            await knowledge_base_service.update_stats(\n                kb_record_id,\n                chunks=chunks,\n                words=words,\n                characters=characters,\n                size_bytes=await backend.storage_size_bytes(),\n                source_types=sorted(existing | extensions),\n            )\n        except Exception as e:  # noqa: BLE001 — stats are cosmetic; chunks are already written\n            self.log(f\"Warning: Could not refresh knowledge base stats: {e}\")\n\n    @staticmethod\n    def _default_backend_selection() -> tuple[str, dict[str, Any]]:\n        \"\"\"Deployment default for a new KB with no explicit backend chosen.\n\n        Environment-driven: pgVector when ``PGVECTOR_CONNECTION_STRING`` is set,\n        else OpenSearch when ``OPENSEARCH_URL`` is set, else SQLite. An\n        unspecified selection (headless flows, multi-replica) therefore lands on\n        the deployment's shared store.\n        \"\"\"\n        from lfx.base.knowledge_bases.backends.postgres import resolve_default_kb_backend\n\n        backend_type = resolve_default_kb_backend()\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, dict(_DEFAULT_OPENSEARCH_CONFIG)\n        return backend_type, {}\n\n    @classmethod\n    def _normalize_backend_selection(cls, value: Any) -> tuple[str, dict[str, Any]]:\n        \"\"\"Normalize a DBProviderInput value into backend type/config.\"\"\"\n        if not value:\n            return cls._default_backend_selection()\n\n        backend_config: dict[str, Any]\n        if isinstance(value, str):\n            backend_type, backend_config = value, {}\n        elif isinstance(value, dict):\n            backend_type = str(value.get(\"backend_type\") or value.get(\"id\") or BackendType.SQLITE.value)\n            backend_config = value.get(\"backend_config\") or value.get(\"config\") or {}\n        else:\n            msg = \"Invalid vector-store backend selection.\"\n            raise TypeError(msg)\n        if not isinstance(backend_config, dict):\n            msg = \"Vector-store backend configuration must be an object.\"\n            raise TypeError(msg)\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, {**_DEFAULT_OPENSEARCH_CONFIG, **backend_config}\n        if backend_type in {BackendType.SQLITE.value, BackendType.POSTGRES.value}:\n            return backend_type, backend_config\n        msg = \"Chroma is retired. Choose SQLite, pgVector, or OpenSearch for a new knowledge base.\"\n        raise ValueError(msg)\n\n    async def _create_knowledge_base_record(\n        self,\n        *,\n        user_id: Any,\n        name: str,\n        model_selection: list[dict[str, Any]],\n        backend_type: str,\n        backend_config: dict[str, Any],\n    ) -> None:\n        \"\"\"Persist the component-created KB in the DB when Langflow is available.\"\"\"\n        from langflow.api.utils import knowledge_base_service\n\n        await knowledge_base_service.create_record(\n            user_id=user_id,\n            name=name,\n            model_selection=model_selection,\n            column_config=_normalize_column_config(self.column_config) if isinstance(self.column_config, list) else [],\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n        \"\"\"Build detailed column metadata.\"\"\"\n        metadata: dict[str, Any] = {\n            \"total_columns\": len(df_source.columns),\n            \"mapped_columns\": len(config_list),\n            \"unmapped_columns\": len(df_source.columns) - len(config_list),\n            \"columns\": [],\n            \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n        }\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            vectorize = coalesce_bool(config.get(\"vectorize\"))\n            identifier = coalesce_bool(config.get(\"identifier\"))\n\n            metadata[\"columns\"].append(\n                {\n                    \"name\": col_name,\n                    \"vectorize\": vectorize,\n                    \"identifier\": identifier,\n                }\n            )\n\n            if vectorize:\n                metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n            if identifier:\n                metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n        return metadata\n\n    async def _create_vector_store(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        \"\"\"Create vector store using the configured DB provider.\"\"\"\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            return await self._ingest_into_backend(backend, df_source, config_list, embedding_function, job_id=job_id)\n        except BaseException:\n            await _finish_despite_cancellation(backend.teardown())\n            raise\n\n    async def _ingest_into_backend(\n        self,\n        backend: BaseVectorStoreBackend,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        await backend.ensure_ready()\n\n        from langflow.services.knowledge_base_storage.runtime import operation, resolve_record\n\n        record = await resolve_record(self._user_uuid, self.knowledge_base)\n        async with operation(record, shared=True):\n            existing_ids = set()\n            if not self.allow_duplicates:\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        for document in batch:\n                            doc_id = document.metadata.get(\"_id\")\n                            if doc_id:\n                                existing_ids.add(doc_id)\n\n        data_objects = await self._convert_df_to_data_objects(df_source, config_list, existing_ids=existing_ids)\n\n        user_metadata_tag = self._resolve_user_metadata_tag()\n\n        documents = []\n        for data_obj in data_objects:\n            doc = data_obj.to_lc_document()\n            if user_metadata_tag:\n                doc.metadata[\"source_metadata\"] = user_metadata_tag\n            documents.append(doc)\n        if not documents:\n            return backend\n\n        # No storage lease may surround the provider call. The write lease\n        # below rechecks routing and IDs after concurrent operations finish.\n        vectors = await self._embed_unless_job_cancelled(\n            embedding_function, [doc.page_content for doc in documents], job_id\n        )\n        if len(vectors) != len(documents):\n            msg = \"Embedding provider returned an incorrect number of vectors\"\n            raise BackendConfigurationError(msg)\n        embedded = [\n            IngestedDocument(doc.page_content, doc.metadata, vector, id=doc.id)\n            for doc, vector in zip(documents, vectors, strict=True)\n        ]\n        async with operation(record):\n            # A cancel that landed after the embeddings returned must still\n            # keep this run's chunks out of the KB.\n            await self._raise_if_job_cancelled(job_id)\n            if not self.allow_duplicates:\n                current_ids = set()\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        current_ids.update(doc.metadata[\"_id\"] for doc in batch if doc.metadata.get(\"_id\"))\n                unique = []\n                for doc in embedded:\n                    doc_id = doc.metadata.get(\"_id\")\n                    if doc_id and doc_id in current_ids:\n                        continue\n                    if doc_id:\n                        current_ids.add(doc_id)\n                    unique.append(doc)\n                embedded = unique\n\n            if embedded:\n                await backend.add_embedded_documents(embedded)\n                self.log(f\"Added {len(embedded)} documents to vector store '{self.knowledge_base}'\")\n\n        return backend\n\n    async def _embed_unless_job_cancelled(\n        self, embedding_function, texts: list[str], job_id: uuid.UUID | None\n    ) -> list[list[float]]:\n        \"\"\"Embed ``texts``, giving up as soon as the run's job is cancelled.\n\n        The Knowledge page cancels a run by marking its job. The embedding call\n        is the long, write-free part of a flow ingestion, so it is the safe\n        place to stop.\n        \"\"\"\n        await self._raise_if_job_cancelled(job_id)\n        if job_id is None:\n            return await embedding_function.aembed_documents(texts)\n\n        # A task group would wrap provider errors in an ExceptionGroup, so a\n        # plain watcher task cancels this scope instead.\n        with anyio.CancelScope() as scope:\n            watcher = asyncio.ensure_future(self._cancel_when_job_cancelled(job_id, scope))\n            try:\n                return await embedding_function.aembed_documents(texts)\n            finally:\n                # The watcher may be mid-poll; let its DB session close.\n                watcher.cancel()\n                with anyio.CancelScope(shield=True):\n                    await asyncio.wait({watcher})\n        raise self._cancelled_error()\n\n    async def _cancel_when_job_cancelled(self, job_id: uuid.UUID, scope: anyio.CancelScope) -> None:\n        # The cancel is a job-row write from another request, so it is polled.\n        while True:\n            await asyncio.sleep(JOB_CANCEL_POLL_SECONDS)\n            if await self._job_cancelled(job_id):\n                scope.cancel()\n                return\n\n    async def _raise_if_job_cancelled(self, job_id: uuid.UUID | None) -> None:\n        if await self._job_cancelled(job_id):\n            raise self._cancelled_error()\n\n    def _cancelled_error(self) -> KnowledgeIngestionCancelledError:\n        return KnowledgeIngestionCancelledError(f\"Ingestion into '{self.knowledge_base}' was cancelled.\")\n\n    async def _job_cancelled(self, job_id: uuid.UUID | None) -> bool:\n        if job_id is None:\n            return False\n        try:\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError:\n            return False\n        try:\n            job = await get_job_service().get_job_by_job_id(job_id)\n        except Exception as exc:  # noqa: BLE001 — a failed poll must not abort the ingestion\n            self.log(f\"Could not check whether ingestion job {job_id} was cancelled: {exc}\")\n            return False\n        return job is not None and job.status == JobStatus.CANCELLED\n\n    async def _convert_df_to_data_objects(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        existing_ids: set[str] | None = None,\n    ) -> list[Data]:\n        \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n        data_objects: list[Data] = []\n\n        if existing_ids is None:\n            existing_ids = set()\n\n        content_cols = []\n        identifier_cols = []\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n\n            if coalesce_bool(config.get(\"vectorize\")):\n                content_cols.append(col_name)\n            if coalesce_bool(config.get(\"identifier\")):\n                identifier_cols.append(col_name)\n\n        for _, row in df_source.iterrows():\n            identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n            page_content = \" \".join(identifier_parts)\n\n            data_dict = {\n                \"text\": page_content,\n            }\n\n            if identifier_cols:\n                identifier_parts = [\n                    str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n                ]\n                page_content = \" \".join(identifier_parts)\n\n            for col in df_source.columns:\n                if col not in content_cols and col in row and self._scalar_notna(row[col]):\n                    value = row[col]\n                    data_dict[col] = str(value)\n\n            page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n            data_dict[\"_id\"] = page_content_hash\n\n            if not self.allow_duplicates and page_content_hash in existing_ids:\n                self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n                continue\n\n            data_obj = Data(data=data_dict)\n            data_objects.append(data_obj)\n\n        return data_objects\n\n    def is_valid_collection_name(self, name: str) -> bool:\n        \"\"\"Return whether ``name`` satisfies the shared collection-name contract.\"\"\"\n        return is_valid_kb_collection_name(name)\n\n    def _resolve_store_path(\n        self,\n        kb_user: str,\n        backend_type: str,\n        backend_config: dict[str, Any] | None,\n    ) -> Path | None:\n        \"\"\"Return this KB's on-disk directory, or ``None`` when it needs no disk.\n\n        Only local Chroma stores anything on this box; every other backend\n        resolves to ``None`` and never touches the filesystem.\n        \"\"\"\n        # Lazy import keeps lfx importable without langflow's DB services.\n        from langflow.api.utils.kb_helpers import resolve_local_store_path\n\n        return resolve_local_store_path(\n            self.knowledge_base,\n            kb_user,\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    async def _kb_username(self) -> str:\n        \"\"\"Resolve the owning user's username (used to root a local-Chroma path).\"\"\"\n        cached = getattr(self, \"_cached_kb_username\", None)\n        if cached is not None:\n            return cached\n\n        # Lazy import to keep ``lfx`` importable standalone — langflow's\n        # user/DB models are not always available at module load time.\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        async with session_scope() as db:\n            if not self.user_id:\n                msg = \"User ID is required for fetching knowledge base path.\"\n                raise ValueError(msg)\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n            self._cached_kb_username = current_user.username\n\n        return self._cached_kb_username\n\n    async def _kb_path(self) -> Path | None:\n        \"\"\"Resolve the KB's local directory from its configured backend.\"\"\"\n        cached_path = getattr(self, \"_cached_kb_path\", None)\n        if cached_path is not None:\n            return cached_path\n\n        backend_type, backend_config = await self._resolve_backend_config()\n        kb_user = await self._kb_username()\n        self._cached_kb_path = self._resolve_store_path(kb_user, backend_type, backend_config)\n        return self._cached_kb_path\n\n    def _resolve_user_metadata_tag(self) -> str:\n        \"\"\"Return the JSON-encoded user metadata tag for chunk writes.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return \"\"\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return \"\"\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError) as exc:\n            self.log(f\"KnowledgeComponent: metadata_json is not valid JSON ({exc}); skipping metadata stamp.\")\n            return \"\"\n        if not isinstance(decoded, dict):\n            self.log(\"KnowledgeComponent: metadata_json must decode to a JSON object; skipping metadata stamp.\")\n            return \"\"\n        return json.dumps(decoded, sort_keys=True)\n\n    async def build_kb_info(self) -> Data:\n        \"\"\"Main ingestion routine → returns a dict with KB metadata.\n\n        The annotation is intentionally narrowed to ``Data`` even though the\n        cross-mode fallback below may return a ``DataFrame`` from\n        ``retrieve_data``. The frontend builds React-Flow handle IDs from\n        this output's type list; widening it to ``Data | DataFrame`` makes\n        the API advertise ``[\"JSON\", \"Table\"]`` for the ingest output and\n        breaks every saved-edge sourceHandle that was generated against\n        a single-type handle (BUG-02). Python doesn't enforce return\n        annotations at runtime, so the rare fallback path keeps working.\n        \"\"\"\n        if _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.retrieve_data()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        run_id: uuid.UUID | None = None\n        run_job_id: uuid.UUID | None = None\n        run_summary: IngestionSummary | None = None\n        run_status: IngestionRunStatus = IngestionRunStatus.SUCCEEDED\n        run_error: str | None = None\n        kb_record_id: uuid.UUID | None = None\n        previous_kb_status = \"ready\"\n        previous_failure_reason: str | None = None\n        try:\n            input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n            df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n            config_list = self._validate_column_config(df_source)\n            column_metadata = self._build_column_metadata(config_list, df_source)\n\n            # Embedding config comes from the ``knowledge_base`` row — the sole\n            # authority. No sidecar is read, so ingestion resolves the same model\n            # on any replica.\n            stored_metadata = await self._get_kb_metadata()\n            # A cancelled run restores this status. Only an earlier failure is\n            # kept; \"empty\" is an API projection of a ready KB with no chunks.\n            if stored_metadata.get(\"status\") == \"failed\":\n                previous_kb_status = \"failed\"\n                previous_failure_reason = stored_metadata.get(\"failure_reason\")\n            model_selection = stored_metadata.get(\"model_selection\")\n            if model_selection:\n                model_selection = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            else:\n                embedding_model_name = stored_metadata.get(\"embedding_model\")\n                embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n                if embedding_model_name:\n                    try:\n                        all_options = get_embedding_model_options(user_id=self.user_id)\n                        match = next(\n                            (o for o in all_options if o.get(\"name\") == embedding_model_name),\n                            None,\n                        )\n                        if match:\n                            model_selection = [match]\n                        else:\n                            self.log(\n                                f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n                                \"from the knowledge base record is no longer available in the model registry. \"\n                                \"Please re-create this knowledge base with a supported embedding model.\"\n                            )\n                            msg = (\n                                f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n                                \"The knowledge base was created with an older format and the model \"\n                                \"is not available in the current registry. \"\n                                \"Please re-create the knowledge base with a supported embedding model.\"\n                            )\n                            raise ValueError(msg)\n                    except ValueError:\n                        raise\n                    except Exception:  # noqa: BLE001\n                        self.log(\n                            f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n                            \"Please re-create this knowledge base with a supported embedding model.\"\n                        )\n                        msg = (\n                            f\"Could not look up embedding model '{embedding_model_name}' \"\n                            f\"(provider: {embedding_provider}). \"\n                            \"Please re-create the knowledge base with a supported embedding model.\"\n                        )\n                        raise ValueError(msg)  # noqa: B904\n\n            if not model_selection:\n                msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n                raise ValueError(msg)\n\n            # ``api_key=None`` falls through to the user's variables table and\n            # then the environment inside ``get_embeddings`` — the same\n            # resolution the server-side ingestion path uses. The component input\n            # only overrides it.\n            embedding_function = get_embeddings(\n                model=model_selection,\n                user_id=self.user_id,\n                api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n                chunk_size=self.chunk_size,\n            )\n\n            kb_path = await self._kb_path()\n\n            run_id, run_job_id, run_summary, kb_record_id = await self._begin_ingestion_run(kb_path)\n            if kb_record_id is not None:\n                await self._record_kb_status(kb_record_id, \"ingesting\")\n\n            backend = await self._create_vector_store(\n                df_source, config_list, embedding_function=embedding_function, job_id=run_job_id\n            )\n\n            try:\n                # Stamp the KB with the file extensions we just ingested so\n                # the Knowledge Bases list renders the correct icon for\n                # flow-driven ingestion (input_df), matching direct upload.\n                # We look at the raw input first because ``convert_to_dataframe``\n                # drops Message/Data metadata fields (file_path, mimetype, …)\n                # when projecting onto the DataFrame.\n                source_types = self._extract_source_types_from_input(input_value)\n                source_types |= self._extract_source_types_from_df(df_source)\n                # Storage routing returns a guarded proxy with the backend's\n                # methods, so a concrete-class check would skip the refresh.\n                await self._refresh_kb_stats(\n                    kb_record_id=kb_record_id,\n                    backend=backend,\n                    extensions=source_types,\n                )\n            finally:\n                await _finish_despite_cancellation(backend.teardown())\n\n            meta: dict[str, Any] = {\n                \"kb_id\": str(uuid.uuid4()),\n                \"kb_name\": self.knowledge_base,\n                \"rows\": len(df_source),\n                \"column_metadata\": column_metadata,\n                \"path\": str(kb_path),\n                \"config_columns\": len(config_list),\n                \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n            }\n\n            if run_summary is not None:\n                run_summary.record_item(\n                    IngestionItemResult(\n                        item_id=self.knowledge_base,\n                        display_name=f\"{self.knowledge_base} ({len(df_source)} rows)\",\n                        status=IngestionItemStatus.SUCCEEDED,\n                        chunks_created=len(df_source),\n                    ),\n                )\n\n            if kb_record_id is not None:\n                # Stats were already refreshed straight from the backend above.\n                await self._record_kb_status(kb_record_id, \"ready\")\n\n            self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n            return Data(data=meta)\n\n        except asyncio.CancelledError:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = \"Flow ingestion was cancelled.\"\n            raise\n        except KnowledgeIngestionCancelledError as e:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = str(e)\n            raise\n        except (OSError, ValueError, RuntimeError, KeyError) as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            msg = f\"Error during KB ingestion: {e}\"\n            raise RuntimeError(msg) from e\n        except Exception as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            raise\n        finally:\n\n            async def settle_run() -> None:\n                if run_id is not None and run_summary is not None:\n                    await self._finalize_ingestion_run(\n                        run_id=run_id,\n                        job_id=run_job_id,\n                        summary=run_summary,\n                        status=run_status,\n                        error_message=run_error,\n                    )\n                if kb_record_id is not None:\n                    if run_status is IngestionRunStatus.FAILED:\n                        await self._record_kb_status(kb_record_id, \"failed\", failure_reason=run_error)\n                    elif run_status is IngestionRunStatus.CANCELLED:\n                        await self._record_kb_status(\n                            kb_record_id, previous_kb_status, failure_reason=previous_failure_reason\n                        )\n\n            # A stopped build can cancel this task more than once, and the\n            # writes that make the run terminal must survive every cancel.\n            await _finish_despite_cancellation(settle_run())\n\n    async def _begin_ingestion_run(\n        self,\n        kb_path: Path,\n    ) -> tuple[uuid.UUID | None, uuid.UUID | None, IngestionSummary | None, uuid.UUID | None]:\n        \"\"\"Create a parent ``Job`` and seed an ingestion-run row.\"\"\"\n        if not self.user_id:\n            self.log(\"No user_id on component; skipping ingestion-run tracking.\")\n            return None, None, None, None\n\n        try:\n            user_uuid = uuid.UUID(str(self.user_id))\n        except (ValueError, TypeError) as exc:\n            self.log(f\"Could not coerce user_id={self.user_id!r} to UUID; skipping run tracking ({exc}).\")\n            return None, None, None, None\n\n        try:\n            from langflow.api.utils import ingestion_run_service, knowledge_base_service\n            from langflow.services.database.models.jobs.model import JobStatus, JobType\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion will not be recorded ({exc}).\")\n            return None, None, None, None\n\n        job_id: uuid.UUID | None = None\n        run_id: uuid.UUID | None = None\n        summary: IngestionSummary | None = None\n        try:\n            kb_record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n            kb_record_id = kb_record.id if kb_record is not None else None\n\n            user_metadata = self._parse_user_metadata_dict()\n\n            job_id = uuid.uuid4()\n            job_service = get_job_service()\n            raw_flow_id = getattr(self, \"flow_id\", None)\n            flow_id_uuid = uuid.UUID(str(raw_flow_id)) if raw_flow_id else job_id\n            source = FlowComponentSource(\n                user_id=user_uuid,\n                source_config={\n                    \"knowledge_base\": self.knowledge_base,\n                    \"kb_path\": str(kb_path),\n                    \"flow_id\": str(flow_id_uuid),\n                },\n            )\n            summary = IngestionSummary(\n                kb_name=self.knowledge_base,\n                source_type=source.source_type.value,\n                user_id=user_uuid,\n                job_id=job_id,\n                source_config=source.describe().get(\"config\") or {},\n                user_metadata=user_metadata,\n            )\n            # Inserted running and already heartbeated, so an orphan sweep in\n            # another worker never sees this live run without a heartbeat.\n            await job_service.create_job(\n                job_id=job_id,\n                flow_id=flow_id_uuid,\n                job_type=JobType.INGESTION,\n                asset_id=kb_record_id,\n                asset_type=\"knowledge_base\",\n                user_id=user_uuid,\n                status=JobStatus.IN_PROGRESS,\n                heartbeat=True,\n            )\n\n            run_id = await ingestion_run_service.create_run(\n                kb_name=self.knowledge_base,\n                source=source,\n                job_id=job_id,\n                user_id=user_uuid,\n                kb_id=kb_record_id,\n                user_metadata=user_metadata,\n            )\n            if run_id is None:\n                return None, job_id, None, kb_record_id\n            await ingestion_run_service.mark_running(run_id)\n\n            self.log(f\"Started ingestion run job_id={job_id} kb_name={self.knowledge_base} kb_id={kb_record_id}\")\n            # Keeps the job heartbeated until _finalize_ingestion_run stops it.\n            await job_service.start_keep_alive(job_id)\n        except asyncio.CancelledError:\n            # The caller has not received the run ID yet, so it cannot finalize\n            # a cancellation that arrives while tracking is being initialized.\n            if job_id is not None and summary is not None:\n                await _finish_despite_cancellation(\n                    self._finalize_ingestion_run(\n                        run_id=run_id or job_id,\n                        job_id=job_id,\n                        summary=summary,\n                        status=IngestionRunStatus.CANCELLED,\n                        error_message=\"Flow ingestion was cancelled.\",\n                    )\n                )\n            raise\n        except Exception as exc:  # noqa: BLE001 — telemetry must never abort ingestion\n            self.log(f\"Could not begin ingestion-run tracking: {exc}\")\n            return None, None, None, None\n        else:\n            return run_id, job_id, summary, kb_record_id\n\n    async def _finalize_ingestion_run(\n        self,\n        *,\n        run_id: uuid.UUID,\n        job_id: uuid.UUID | None,\n        summary: IngestionSummary,\n        status: IngestionRunStatus,\n        error_message: str | None,\n    ) -> None:\n        \"\"\"Persist the final summary and transition the parent Job.\"\"\"\n        try:\n            from langflow.api.utils import ingestion_run_service\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion-run finalize skipped ({exc}).\")\n            return\n\n        try:\n            if job_id is not None:\n                # The heartbeat also rewrites job_metadata, so it stops first.\n                await get_job_service().stop_keep_alive(job_id)\n            await ingestion_run_service.finalize_run(\n                run_id,\n                summary=summary,\n                status=status,\n                error_message=error_message,\n            )\n            if job_id is not None:\n                terminal_status = {\n                    IngestionRunStatus.FAILED: JobStatus.FAILED,\n                    IngestionRunStatus.CANCELLED: JobStatus.CANCELLED,\n                }.get(status, JobStatus.COMPLETED)\n                await get_job_service().update_job_status(job_id, terminal_status, finished_timestamp=True)\n        except Exception as exc:  # noqa: BLE001 — telemetry must never re-raise\n            self.log(f\"Could not finalize ingestion-run tracking: {exc}\")\n\n    async def _record_kb_status(\n        self,\n        kb_record_id: uuid.UUID,\n        status_value: str,\n        *,\n        failure_reason: str | None = None,\n    ) -> None:\n        \"\"\"Mirror Path A's KB-row status transitions.\"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n            from langflow.services.database.models.knowledge_base.model import KnowledgeBaseStatus\n        except ImportError:\n            return\n        try:\n            await knowledge_base_service.update_status(\n                kb_record_id,\n                status=KnowledgeBaseStatus(status_value),\n                failure_reason=failure_reason,\n            )\n        except Exception as exc:  # noqa: BLE001\n            self.log(f\"Could not update KB status to {status_value}: {exc}\")\n\n    def _parse_user_metadata_dict(self) -> dict[str, Any]:\n        \"\"\"Decode ``metadata_json`` to a dict, or ``{}`` on any error.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return {}\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return {}\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError):\n            return {}\n        return decoded if isinstance(decoded, dict) else {}\n\n    # =====================================================================\n    #                       RETRIEVAL CODE PATH\n    # =====================================================================\n    @property\n    def _user_uuid(self) -> uuid.UUID | None:\n        \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n        if not self.user_id:\n            return None\n        return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n    async def _get_kb_metadata(self, knowledge_base: str | None = None) -> dict:\n        \"\"\"Load this knowledge base's embedding config from its database row.\n\n        The row is the sole authority. There is no on-disk sidecar to fall back\n        to, which is what lets retrieval work identically on a replica whose\n        filesystem never held the KB directory.\n        \"\"\"\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return {}\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return {}\n        record = await knowledge_base_service.get_by_user_and_name(\n            user_uuid,\n            knowledge_base or self.knowledge_base,\n        )\n        if record is None:\n            return {}\n        return knowledge_base_service.record_to_metadata_dict(record)\n\n    async def _backend_from_record(self) -> tuple[str, dict[str, Any]] | None:\n        \"\"\"Read ``(backend_type, backend_config)`` off the KB's database row.\n\n        Returns ``None`` when there is no row to read — a legacy KB predating the\n        record, or bare lfx with no langflow installed. A lookup that *fails* is a\n        different situation and is allowed to propagate: guessing a backend after a\n        database error is how vectors end up in a store nothing queries.\n        \"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return None\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return None\n        record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n        if record is None:\n            return None\n        return record.backend_type or BackendType.CHROMA.value, record.backend_config or {}\n\n    async def _resolve_backend_config(self) -> tuple[str, dict[str, Any]]:\n        \"\"\"Resolve this KB's backend from its database row.\n\n        Ingestion and retrieval both land here, so they cannot disagree about\n        where this KB's vectors live. A backend that cannot be resolved raises\n        rather than defaulting to local storage: guessing is how a pod ends up\n        writing vectors to a local directory while queries follow the real config\n        to a remote store and silently find nothing.\n        \"\"\"\n        resolved = await self._backend_from_record()\n        if resolved is None:\n            msg = (\n                f\"Cannot determine the vector-store backend for knowledge base \"\n                f\"'{self.knowledge_base}': it has no database record. Refusing to fall \"\n                f\"back to local storage, which would write to a different store than \"\n                f\"queries read from. If this knowledge base predates database-backed \"\n                f\"metadata, adopt it with 'langflow reconcile-kb-from-disk'.\"\n            )\n            raise ValueError(msg)\n        return resolved\n\n    def _resolve_model_selection(self, metadata: dict[str, Any]) -> list[dict[str, Any]]:\n        \"\"\"Resolve the ``get_embeddings``-compatible model selection from metadata.\"\"\"\n        model_selection = metadata.get(\"model_selection\")\n        if model_selection:\n            selection_list = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            return [self._hydrate_model_metadata(entry) for entry in selection_list]\n\n        embedding_model_name = metadata.get(\"embedding_model\")\n        embedding_provider = metadata.get(\"embedding_provider\", \"Unknown\")\n        if not embedding_model_name:\n            msg = (\n                f\"Knowledge base '{self.knowledge_base}' has no embedding model recorded; \"\n                \"re-create it with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n\n        match = self._find_catalog_entry(embedding_model_name)\n        if match is None:\n            msg = (\n                f\"Embedding model '{embedding_model_name}' (provider '{embedding_provider}') \"\n                \"recorded for this knowledge base is no longer available in the model registry. \"\n                \"Please re-create the knowledge base with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n        return [match]\n\n    def _hydrate_model_metadata(self, entry: dict[str, Any]) -> dict[str, Any]:\n        \"\"\"Fill in ``metadata.embedding_class`` / ``param_mapping`` if missing.\"\"\"\n        entry_metadata = entry.get(\"metadata\") or {}\n        has_class = bool(entry_metadata.get(\"embedding_class\"))\n        has_mapping = bool(entry_metadata.get(\"param_mapping\"))\n        if has_class and has_mapping:\n            return entry\n\n        model_name = entry.get(\"name\")\n        if not model_name:\n            return entry\n\n        catalog_entry = self._find_catalog_entry(model_name)\n        if catalog_entry is None:\n            return entry\n\n        catalog_metadata = catalog_entry.get(\"metadata\") or {}\n        merged_metadata = {**catalog_metadata, **entry_metadata}\n        return {**entry, \"metadata\": merged_metadata}\n\n    def _find_catalog_entry(self, model_name: str) -> dict[str, Any] | None:\n        \"\"\"Look up an embedding model by name in the unified-models catalog.\"\"\"\n        options = get_embedding_model_options(user_id=self.user_id)\n        return next((o for o in options if o.get(\"name\") == model_name), None)\n\n    async def retrieve_data(self) -> DataFrame:\n        \"\"\"Retrieve data from the selected knowledge base.\n\n        Annotation narrowed to ``DataFrame`` to keep this output's handle\n        type list at ``[\"Table\"]`` only; widening to a union surfaces\n        ``[\"Table\", \"JSON\"]`` on the API and breaks every starter-project\n        edge whose sourceHandle was stored against a single-type handle\n        (BUG-02). The cross-mode defensive fallback may still return a\n        ``Data`` at runtime — Python ignores return annotations, so\n        nothing breaks.\n        \"\"\"\n        if not _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.build_kb_info()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        if not self.user_id:\n            msg = \"User ID is required for fetching Knowledge Base data.\"\n            raise ValueError(msg)\n\n        # Saved flows can load in standalone lfx, but knowledge retrieval\n        # requires the application's user database and storage services.\n        try:\n            from langflow.services.database.models.user.crud import get_user_by_id\n        except ModuleNotFoundError as exc:\n            if exc.name != \"langflow\":\n                raise\n            msg = \"Knowledge Base retrieval requires OpenXFlow's application services. Run this flow in OpenXFlow.\"\n            raise RuntimeError(msg) from None\n\n        async with session_scope() as db:\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n\n        metadata = await self._get_kb_metadata()\n        if not metadata:\n            msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n            raise ValueError(msg)\n\n        model_selection = self._resolve_model_selection(metadata)\n        chunk_size = metadata.get(\"chunk_size\")\n\n        # Resolve where this KB lives before instantiating embeddings: path\n        # containment is a cheap local check, and a request that will be refused\n        # shouldn't first pay for a credential lookup and a provider client.\n        backend_type, _backend_config = await self._resolve_backend_config()\n\n        # ``api_key=None`` is not \"no credential\": ``get_embeddings`` resolves the\n        # provider's key from the user's variables table and then the environment,\n        # exactly as the server-side ingestion path does. The component input just\n        # takes precedence when supplied.\n        embedding_function = get_embeddings(\n            model=model_selection,\n            user_id=self.user_id,\n            api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n            chunk_size=chunk_size,\n        )\n\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            user_metadata_filter = _parse_metadata_filter(getattr(self, \"metadata_filter\", None))\n            use_scores = bool(self.search_query)\n            exact_filter = backend_type == BackendType.SQLITE.value\n            search_k = self.top_k if exact_filter or not user_metadata_filter else self.top_k * 4\n            search_options = {\"source_filter\": user_metadata_filter} if exact_filter else {}\n            results = await backend.similarity_search(\n                query=self.search_query or \"\",\n                k=search_k,\n                with_scores=use_scores,\n                **search_options,\n            )\n            if user_metadata_filter and not exact_filter:\n                results = [\n                    (doc, score) for doc, score in results if _chunk_matches_filter(doc.metadata, user_metadata_filter)\n                ][: self.top_k]\n\n            embeddings_by_key: dict[tuple[str, str], list[float]] = {}\n            if self.include_embeddings and results:\n                # Join each retrieved chunk to its stored embedding. We key on\n                # ``_id`` when present and fall back to page content otherwise,\n                # so KBs populated by direct file upload — whose chunks carry no\n                # ``_id`` — still resolve. See ``_embedding_match_key``.\n                wanted_keys = {_embedding_match_key(doc.page_content, doc.metadata) for doc, _score in results}\n                async with aclosing(backend.iter_documents(include_embeddings=True)) as batches:\n                    async for batch in batches:\n                        for entry in batch:\n                            if entry.embedding is None:\n                                continue\n                            key = _embedding_match_key(entry.content, entry.metadata)\n                            if key in wanted_keys:\n                                embeddings_by_key[key] = entry.embedding\n                        if wanted_keys <= embeddings_by_key.keys():\n                            break\n\n            data_list: list[Data] = []\n            for doc, score in results:\n                kwargs: dict[str, Any] = {\"content\": doc.page_content}\n                if use_scores:\n                    kwargs[\"_score\"] = backend.normalize_score(score)\n                if self.include_metadata:\n                    kwargs.update(doc.metadata)\n                if self.include_embeddings:\n                    kwargs[\"_embeddings\"] = embeddings_by_key.get(_embedding_match_key(doc.page_content, doc.metadata))\n                data_list.append(Data(**kwargs))\n\n            return DataFrame(data=data_list)\n        finally:\n            await backend.teardown()\n\n\ndef _embedding_match_key(content: str, metadata: dict[str, Any] | None) -> tuple[str, str]:\n    \"\"\"Build a stable key for aligning a retrieved chunk with its stored embedding.\n\n    Embeddings are gathered separately (via ``iter_documents``) and then joined\n    back onto the search results. Component-driven ingestion stamps a\n    content-hash ``_id`` on every chunk, but direct file-upload ingestion\n    (``KBIngestionHelper.perform_ingestion``) does not. Keying the join purely on\n    ``_id`` therefore left every upload-populated KB with ``_embeddings: None``.\n\n    We prefer ``_id`` when present (so legitimately distinct chunks that happen to\n    share text aren't collapsed) and fall back to the chunk's page content\n    otherwise. The content fallback is exact for the embedding use case: two\n    chunks with identical text necessarily share the same embedding vector. The\n    leading ``\"id\"`` / ``\"content\"`` tag namespaces the two key spaces so a mixed\n    KB (some chunks with ``_id``, some without) never cross-matches.\n    \"\"\"\n    doc_id = metadata.get(\"_id\") if metadata else None\n    if doc_id:\n        return (\"id\", str(doc_id))\n    return (\"content\", content or \"\")\n\n\ndef _parse_metadata_filter(raw: str | None) -> dict[str, list[str]]:\n    \"\"\"Decode the ``metadata_filter`` input into a {key: [values]} map.\n\n    Empty or malformed input maps to an empty filter so retrieval falls back\n    to the unfiltered path. JSON errors are swallowed here rather than raised:\n    surfacing component-config errors at the canvas node would break a flow\n    run for what is meant to be an optional refinement.\n    \"\"\"\n    if not raw:\n        return {}\n    text = raw.strip() if isinstance(raw, str) else raw\n    if not text:\n        return {}\n    try:\n        decoded = json.loads(text)\n    except (TypeError, json.JSONDecodeError):\n        logger.warning(\"KnowledgeComponent: metadata_filter is not valid JSON; ignoring filter.\")\n        return {}\n    if not isinstance(decoded, dict):\n        logger.warning(\"KnowledgeComponent: metadata_filter must be a JSON object; ignoring filter.\")\n        return {}\n    result: dict[str, list[str]] = {}\n    for key, value in decoded.items():\n        if not isinstance(key, str):\n            continue\n        if isinstance(value, list):\n            result[key] = [str(entry) for entry in value]\n        else:\n            result[key] = [str(value)]\n    return result\n\n\ndef _chunk_matches_filter(metadata: dict[str, Any] | None, filt: dict[str, list[str]]) -> bool:\n    \"\"\"AND across keys, OR within key values, mirroring the chunks endpoint.\"\"\"\n    if not filt:\n        return True\n    if not metadata:\n        return False\n    raw = metadata.get(\"source_metadata\")\n    if not raw:\n        return False\n    try:\n        stored = json.loads(raw) if isinstance(raw, str) else raw\n    except json.JSONDecodeError:\n        return False\n    if not isinstance(stored, dict):\n        return False\n    for key, expected_values in filt.items():\n        actual = stored.get(key)\n        if actual is None:\n            return False\n        actual_set = {str(entry) for entry in actual} if isinstance(actual, list) else {str(actual)}\n        if not actual_set & set(expected_values):\n            return False\n    return True\n"
               },
               "column_config": {
                 "_input_type": "TableInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json
index f63693a9a4..bd40643dda 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
@@ -450,6 +450,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -458,7 +459,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -467,8 +468,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -741,11 +741,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -757,13 +758,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -913,7 +913,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2184,6 +2184,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -2200,7 +2201,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -2213,8 +2214,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2611,4 +2611,4 @@
     "assistants",
     "agents"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json
index 52d0933225..46e343bccd 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json	
@@ -393,6 +393,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -401,7 +402,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -410,8 +411,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -878,11 +878,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -894,13 +895,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1050,7 +1050,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2233,4 +2233,4 @@
     "chatbots",
     "content-generation"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json
index 92dae4a358..39498f8f66 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
@@ -394,6 +394,7 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -402,7 +403,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -411,8 +412,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -724,11 +724,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -740,13 +741,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -896,7 +896,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1845,4 +1845,4 @@
     "openai",
     "assistants"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Multi Agent Flow.json b/src/backend/base/langflow/initial_setup/starter_projects/Multi Agent Flow.json
index f9036f5c41..354aea1686 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Multi Agent Flow.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Multi Agent Flow.json	
@@ -419,6 +419,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -427,7 +428,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -436,8 +437,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -713,11 +713,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -729,13 +730,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -885,7 +885,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1493,11 +1493,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1509,13 +1510,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1665,7 +1665,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2273,11 +2273,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2289,13 +2290,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2445,7 +2445,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -3061,4 +3061,4 @@
     "agents",
     "chatbots"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json
index bf104a5e21..6f5d34f1e6 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json	
@@ -95,6 +95,7 @@
             "lf_version": "1.6.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -103,7 +104,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -112,8 +113,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -401,7 +401,8 @@
             "legacy": false,
             "lf_version": "1.10.0",
             "metadata": {
-              "code_hash": "6d53589f01c8",
+              "code_hash": "1bc59574381f",
+              "component_class_name": "FileComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -410,7 +411,7 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   },
                   {
                     "name": "pydantic",
@@ -423,8 +424,7 @@
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.files_and_knowledge.file.FileComponent",
-              "component_class_name": "FileComponent"
+              "module": "lfx.components.files_and_knowledge.file.FileComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -563,7 +563,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main OpenXFlow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = str(file.path)\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [str(f.path) for f in file_list]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
+                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = file.path.as_posix() if not file.path.is_absolute() else str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = (\n                    file.path.as_posix()\n                    if settings.storage_type == \"s3\" and not file.path.is_absolute()\n                    else str(file.path)\n                )\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [\n            f.path.as_posix() if settings.storage_type == \"s3\" and not f.path.is_absolute() else str(f.path)\n            for f in file_list\n        ]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
               },
               "concurrency_multithreading": {
                 "_input_type": "IntInput",
@@ -1113,11 +1113,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1129,13 +1130,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1285,7 +1285,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1901,4 +1901,4 @@
     "chatbots",
     "coding"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json
index 75864595d8..820f2bf25a 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json	
@@ -458,6 +458,7 @@
             "lf_version": "1.3.2",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -466,7 +467,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -475,8 +476,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -758,11 +758,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -774,13 +775,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -930,7 +930,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1430,6 +1430,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1446,7 +1447,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -1459,8 +1460,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1840,7 +1840,8 @@
             "icon": "layout-template",
             "legacy": false,
             "metadata": {
-              "code_hash": "a6429c7f9670",
+              "code_hash": "8052088025a1",
+              "component_class_name": "URLComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1849,21 +1850,20 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
-                  },
-                  {
-                    "name": "markitdown",
-                    "version": "0.1.6"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "lfx",
                     "version": null
+                  },
+                  {
+                    "name": "markitdown",
+                    "version": "0.1.6"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.data_source.url.URLComponent",
-              "component_class_name": "URLComponent"
+              "module": "lfx.components.data_source.url.URLComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1947,7 +1947,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom markitdown import MarkItDown\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies OpenXFlow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
+                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        # markitdown imports magika, which loads the nearest .env at import time. Keep that\n        # optional converter out of component discovery and load it only when Markdown output\n        # is actually requested.\n        from markitdown import MarkItDown\n\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
               },
               "continue_on_failure": {
                 "_input_type": "BoolInput",
@@ -2592,4 +2592,4 @@
     "web-scraping",
     "agents"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json
index 911707acf0..d1a07e9a72 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json	
@@ -921,7 +921,7 @@
             "description": "# 📖 README\nThis template translates research paper summaries on ArXiv into Portuguese and summarizes them. \n Using **OpenXFlow’s looping mechanism**, the template iterates through multiple research papers, translates them with the **OpenAI** model component, and outputs an aggregated version of all translated papers.  \n\n## Quick start\n1. Configure your **Model Provider** with your API credentials. \n2. In the **Playground**, enter a query related to a research topic (for example, “Quantum Computing Advancements”).  \n\n  The flow fetches a list of research papers from ArXiv matching the query. Each paper in the retrieved list is processed one-by-one using the OpenXFlow **Loop component**. \n\n  The abstract of each paper is translated into Portuguese by the **OpenAI** model component. \n\n Once all papers are translated, the system aggregates them into a **single structured output**.",
             "display_name": "",
             "documentation": "",
-            "i18n_key": "template_notes.research_translation_loop.60bb882f",
+            "i18n_key": "template_notes.research_translation_loop.c0723e26",
             "template": {}
           },
           "type": "note"
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json
index 353344eb7a..aab6800aa9 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json	
@@ -404,6 +404,7 @@
             "lf_version": "1.4.2",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -412,7 +413,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -421,8 +422,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -696,11 +696,12 @@
             "legacy": false,
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -712,13 +713,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -868,7 +868,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1476,4 +1476,4 @@
     "chatbots",
     "assistants"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json
index f8600803df..9d185005a6 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
@@ -123,6 +123,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -131,7 +132,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -140,8 +141,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -423,11 +423,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -439,13 +440,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -595,7 +595,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1649,4 +1649,4 @@
     "agents",
     "assistants"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json
index b910c89c41..df9f7115e2 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json	
@@ -246,11 +246,12 @@
             "legacy": false,
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -262,13 +263,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -418,7 +418,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1015,11 +1015,12 @@
             "legacy": false,
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1031,13 +1032,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1187,7 +1187,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2270,11 +2270,12 @@
             "legacy": false,
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2286,13 +2287,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2442,7 +2442,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -3029,6 +3029,7 @@
             "legacy": false,
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -3045,7 +3046,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -3058,8 +3059,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -3467,6 +3467,7 @@
             "legacy": false,
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -3475,7 +3476,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -3484,8 +3485,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -3758,4 +3758,4 @@
     "agents",
     "web-scraping"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json
index 84cb8e8d54..9cbaf51e9e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json	
@@ -430,6 +430,7 @@
             "legacy": false,
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -438,7 +439,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -447,8 +448,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -725,11 +725,12 @@
             "legacy": false,
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -741,13 +742,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -897,7 +897,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1374,7 +1374,8 @@
             "icon": "layout-template",
             "legacy": false,
             "metadata": {
-              "code_hash": "a6429c7f9670",
+              "code_hash": "8052088025a1",
+              "component_class_name": "URLComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1383,21 +1384,20 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
-                  },
-                  {
-                    "name": "markitdown",
-                    "version": "0.1.6"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "lfx",
                     "version": null
+                  },
+                  {
+                    "name": "markitdown",
+                    "version": "0.1.6"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.data_source.url.URLComponent",
-              "component_class_name": "URLComponent"
+              "module": "lfx.components.data_source.url.URLComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1481,7 +1481,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom markitdown import MarkItDown\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies OpenXFlow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
+                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        # markitdown imports magika, which loads the nearest .env at import time. Keep that\n        # optional converter out of component discovery and load it only when Markdown output\n        # is actually requested.\n        from markitdown import MarkItDown\n\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
               },
               "continue_on_failure": {
                 "_input_type": "BoolInput",
@@ -1828,6 +1828,7 @@
             "legacy": false,
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1844,7 +1845,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -1857,8 +1858,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2255,4 +2255,4 @@
     "assistants",
     "agents"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json
index c5e09b5cff..0d4766c1ec 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json	
@@ -426,6 +426,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -434,7 +435,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -443,8 +444,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -721,11 +721,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -737,13 +738,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -893,7 +893,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1368,6 +1368,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1384,7 +1385,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -1397,8 +1398,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1778,7 +1778,8 @@
             "icon": "layout-template",
             "legacy": false,
             "metadata": {
-              "code_hash": "a6429c7f9670",
+              "code_hash": "8052088025a1",
+              "component_class_name": "URLComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1787,21 +1788,20 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
-                  },
-                  {
-                    "name": "markitdown",
-                    "version": "0.1.6"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "lfx",
                     "version": null
+                  },
+                  {
+                    "name": "markitdown",
+                    "version": "0.1.6"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.data_source.url.URLComponent",
-              "component_class_name": "URLComponent"
+              "module": "lfx.components.data_source.url.URLComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1885,7 +1885,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom markitdown import MarkItDown\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies OpenXFlow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
+                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        # markitdown imports magika, which loads the nearest .env at import time. Keep that\n        # optional converter out of component discovery and load it only when Markdown output\n        # is actually requested.\n        from markitdown import MarkItDown\n\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
               },
               "continue_on_failure": {
                 "_input_type": "BoolInput",
@@ -2259,4 +2259,4 @@
     "agent",
     "assistants"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json
index f165aa267a..0cbfd9f269 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json	
@@ -395,6 +395,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -403,7 +404,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -412,8 +413,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -680,6 +680,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -688,7 +689,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -697,8 +698,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -983,7 +983,8 @@
             "legacy": false,
             "lf_version": "1.10.0",
             "metadata": {
-              "code_hash": "6d53589f01c8",
+              "code_hash": "1bc59574381f",
+              "component_class_name": "FileComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -992,7 +993,7 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   },
                   {
                     "name": "pydantic",
@@ -1005,8 +1006,7 @@
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.files_and_knowledge.file.FileComponent",
-              "component_class_name": "FileComponent"
+              "module": "lfx.components.files_and_knowledge.file.FileComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1145,7 +1145,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main OpenXFlow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = str(file.path)\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [str(f.path) for f in file_list]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
+                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = file.path.as_posix() if not file.path.is_absolute() else str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = (\n                    file.path.as_posix()\n                    if settings.storage_type == \"s3\" and not file.path.is_absolute()\n                    else str(file.path)\n                )\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [\n            f.path.as_posix() if settings.storage_type == \"s3\" and not f.path.is_absolute() else str(f.path)\n            for f in file_list\n        ]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
               },
               "concurrency_multithreading": {
                 "_input_type": "IntInput",
@@ -1695,11 +1695,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1711,13 +1712,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1867,7 +1867,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2475,11 +2475,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2491,13 +2492,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2647,7 +2647,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -3255,11 +3255,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -3271,13 +3272,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -3427,7 +3427,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -4042,4 +4042,4 @@
   "tags": [
     "classification"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json
index d6d64aefb8..25efe347a4 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
@@ -498,6 +498,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -506,7 +507,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -515,8 +516,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -783,7 +783,8 @@
             "icon": "layout-template",
             "legacy": false,
             "metadata": {
-              "code_hash": "a6429c7f9670",
+              "code_hash": "8052088025a1",
+              "component_class_name": "URLComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -792,21 +793,20 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
-                  },
-                  {
-                    "name": "markitdown",
-                    "version": "0.1.6"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "lfx",
                     "version": null
+                  },
+                  {
+                    "name": "markitdown",
+                    "version": "0.1.6"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.data_source.url.URLComponent",
-              "component_class_name": "URLComponent"
+              "module": "lfx.components.data_source.url.URLComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -890,7 +890,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom markitdown import MarkItDown\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies OpenXFlow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
+                "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        # markitdown imports magika, which loads the nearest .env at import time. Keep that\n        # optional converter out of component discovery and load it only when Markdown output\n        # is actually requested.\n        from markitdown import MarkItDown\n\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
               },
               "continue_on_failure": {
                 "_input_type": "BoolInput",
@@ -1416,11 +1416,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1432,13 +1433,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1588,7 +1588,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2072,11 +2072,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2088,13 +2089,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2244,7 +2244,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2728,11 +2728,12 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -2744,13 +2745,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -2900,7 +2900,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -3374,6 +3374,7 @@
             "lf_version": "1.10.0",
             "metadata": {
               "code_hash": "523dd690abca",
+              "component_class_name": "WebSearchComponent",
               "dependencies": {
                 "dependencies": [
                   {
@@ -3390,7 +3391,7 @@
                   },
                   {
                     "name": "bs4",
-                    "version": "4.12.3"
+                    "version": "4.15.0"
                   },
                   {
                     "name": "pydantic",
@@ -3403,8 +3404,7 @@
                 ],
                 "total_dependencies": 6
               },
-              "module": "lfx.components.data_source.web_search.WebSearchComponent",
-              "component_class_name": "WebSearchComponent"
+              "module": "lfx.components.data_source.web_search.WebSearchComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -3881,4 +3881,4 @@
   "tags": [
     "agents"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json
index ad3071f288..466c5d7fa1 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json	
@@ -404,6 +404,7 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -412,7 +413,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -421,8 +422,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "minimized": true,
             "output_types": [],
@@ -1012,11 +1012,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1028,13 +1029,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1184,7 +1184,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -1876,4 +1876,4 @@
     "chatbots",
     "content-generation"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json
index 10b8caf6b0..bc78f87b57 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
@@ -690,6 +690,7 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "0aab1c78e1dc",
+              "component_class_name": "ChatOutput",
               "dependencies": {
                 "dependencies": [
                   {
@@ -698,7 +699,7 @@
                   },
                   {
                     "name": "fastapi",
-                    "version": "0.139.0"
+                    "version": "0.139.2"
                   },
                   {
                     "name": "lfx",
@@ -707,8 +708,7 @@
                 ],
                 "total_dependencies": 3
               },
-              "module": "lfx.components.input_output.chat_output.ChatOutput",
-              "component_class_name": "ChatOutput"
+              "module": "lfx.components.input_output.chat_output.ChatOutput"
             },
             "output_types": [],
             "outputs": [
@@ -1168,7 +1168,7 @@
             "legacy": false,
             "lf_version": "1.10.1rc3",
             "metadata": {
-              "code_hash": "b41636ff3a48",
+              "code_hash": "53a28a3025de",
               "component_class_name": "KnowledgeComponent",
               "dependencies": {
                 "dependencies": [
@@ -1308,7 +1308,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Unified Knowledge component — ingest into or retrieve from a knowledge base.\n\nThis component merges what used to live in ``ingestion.py`` and\n``retrieval.py`` into a single, mode-driven component. A ``TabInput``\n(\"📥 Ingest\" / \"🔍 Retrieve\") drives which inputs and which output are\nvisible. The merged shape gives users one node per KB instead of two\nparallel ones that always shared the same ``knowledge_base`` picker,\nembedding-model metadata, and backend registry.\n\nBoth legacy classes (``KnowledgeIngestionComponent``,\n``KnowledgeBaseComponent``) remain importable as thin subclasses of\n``KnowledgeComponent`` — see the sibling ``ingestion.py`` / ``retrieval.py``\nmodules — so saved flows continue to load unchanged.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport uuid\nfrom collections.abc import Awaitable, Mapping\nfrom contextlib import aclosing\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, TypeVar\n\nimport anyio\nimport pandas as pd\n\nfrom lfx.base.knowledge_bases.backends import BackendType, BaseVectorStoreBackend\nfrom lfx.base.knowledge_bases.backends.base import BackendConfigurationError, IngestedDocument\nfrom lfx.base.knowledge_bases.backends.naming import ensure_storage_routing_allowed\nfrom lfx.base.knowledge_bases.ingestion_sources.base import (\n    IngestionItemResult,\n    IngestionItemStatus,\n    IngestionRunStatus,\n    IngestionSummary,\n)\nfrom lfx.base.knowledge_bases.ingestion_sources.flow_component import FlowComponentSource\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.knowledge_bases.validation import (\n    is_valid_collection_name as is_valid_kb_collection_name,\n)\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.helpers.base_model import coalesce_bool\nfrom lfx.io import (\n    BoolInput,\n    DBProviderInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MessageTextInput,\n    ModelInput,\n    Output,\n    SecretStrInput,\n    StrInput,\n    TabInput,\n    TableInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n    session_scope,\n)\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n\ndef _inputs_for_mode(default_mode: str) -> list:\n    \"\"\"Return a fresh copy of the canonical inputs list with show flags set for the given mode.\n\n    Used by the legacy subclasses so a saved flow keyed on\n    ``KnowledgeIngestion`` / ``KnowledgeBase`` lands on a node template\n    whose default visibility already matches the pinned mode — no\n    \"wait for the user to click the mode tab\" UX gap on load.\n    \"\"\"\n    always_visible = set(KnowledgeComponent.default_keys) | set(KnowledgeComponent.mode_config[default_mode])\n    inputs_copy = []\n    for inp in KnowledgeComponent.inputs:\n        clone = inp.model_copy(deep=True) if hasattr(inp, \"model_copy\") else inp\n        if clone.name == \"mode\":\n            clone.value = default_mode\n        else:\n            clone.show = clone.name in always_visible\n        inputs_copy.append(clone)\n    return inputs_copy\n\n\nif TYPE_CHECKING:\n    from pathlib import Path\n\n# Mode constants. Plain-text labels (no emoji) for consistency with the rest of\n# OpenXFlow's TabInput palette — see ``MemoryComponent`` for the same convention.\nMODE_INGEST = \"Ingest\"\nMODE_RETRIEVE = \"Retrieve\"\n\n# How often a flow ingestion checks whether its job was cancelled from the\n# Knowledge page while it waits on the embedding provider.\nJOB_CANCEL_POLL_SECONDS = 1.0\n\n\nclass KnowledgeIngestionCancelledError(Exception):\n    \"\"\"The ingestion's job was cancelled from the Knowledge page.\"\"\"\n\n\n_T = TypeVar(\"_T\")\n\n\nasync def _finish_despite_cancellation(awaitable: Awaitable[_T]) -> _T:\n    \"\"\"Run ``awaitable`` to completion even if the caller is cancelled meanwhile.\n\n    ``anyio.CancelScope(shield=True)`` holds off AnyIO cancellation, but a\n    repeated native ``Task.cancel()`` still interrupts it, and a stopped flow\n    build can cancel its task more than once. The work runs in its own task,\n    and the caller waits through every cancellation, re-raising once the work\n    has landed.\n    \"\"\"\n    task = asyncio.ensure_future(awaitable)\n    cancelled = False\n    with anyio.CancelScope(shield=True):\n        while not task.done():\n            try:\n                await asyncio.wait({task})\n            except asyncio.CancelledError:\n                cancelled = True\n    if cancelled:\n        error = None if task.cancelled() else task.exception()\n        if error is not None:\n            logger.warning(f\"Work finished during cancellation failed: {error!r}\")\n        raise asyncio.CancelledError from error\n    return task.result()\n\n\ndef _is_retrieve_mode(value: Any) -> bool:\n    \"\"\"Lenient mode check: treats any label containing 'Retrieve' as retrieve mode.\n\n    Older saved flows may carry the emoji-prefixed labels (\"📥 Ingest\" /\n    \"🔍 Retrieve\") this component used to ship with; substring matching\n    keeps those loading without forcing a flow rewrite.\n    \"\"\"\n    return isinstance(value, str) and \"Retrieve\" in value\n\n\n# Boolean flags of a ``column_config`` row. Toggled cells hold real booleans,\n# but a typed cell (or a flow saved while the cell rendered as plain text)\n# keeps the raw string, e.g. ``\"true\"`` — read them with ``coalesce_bool``.\n_COLUMN_FLAGS = (\"vectorize\", \"identifier\")\n\n\ndef _normalize_column_config(config_list: list[dict[str, Any]]) -> list[dict[str, Any]]:\n    \"\"\"Return copies of the rows with every flag as a real boolean.\"\"\"\n    return [{**row, **{key: coalesce_bool(row.get(key)) for key in _COLUMN_FLAGS}} for row in config_list]\n\n\n# Error message used by both the ingest and retrieve paths when the user is\n# running against an Astra cloud environment that disables these flows.\nastra_error_msg = \"Knowledge ingestion and retrieval are not supported in Astra cloud environment.\"\n\n_DEFAULT_OPENSEARCH_CONFIG = {\n    \"url_variable\": \"OPENSEARCH_URL\",\n    \"username_variable\": \"OPENSEARCH_USERNAME\",\n    \"password_variable\": \"OPENSEARCH_PASSWORD\",  # pragma: allowlist secret\n    \"index_name\": \"\",\n    \"vector_field\": \"vector_field\",\n    \"text_field\": \"text\",\n}\n\n\nclass KnowledgeComponent(Component):\n    \"\"\"One component for both writing into and reading from an OpenXFlow knowledge base.\n\n    A ``TabInput`` switches between ingestion and retrieval. The\n    ``update_build_config`` / ``update_outputs`` hooks hide the inputs\n    and the output that don't apply to the current mode so the canvas\n    node stays focused.\n    \"\"\"\n\n    display_name = \"Knowledge\"\n    description = \"Ingest into or retrieve from an OpenXFlow knowledge base.\"\n    icon = \"database\"\n    name = \"Knowledge\"\n\n    # ------ Mode → visible-fields wiring ---------------------------------\n    # ``default_keys`` are inputs always visible regardless of mode.\n    # ``mode_config`` lists the inputs unique to each mode; everything outside\n    # this set is hidden when its mode is not selected.\n    default_keys: list[str] = [\"mode\", \"knowledge_base\"]\n    mode_config: dict[str, list[str]] = {\n        MODE_INGEST: [\n            \"input_df\",\n            \"column_config\",\n            \"chunk_size\",\n            \"api_key\",\n            \"allow_duplicates\",\n            \"metadata_json\",\n        ],\n        MODE_RETRIEVE: [\n            \"search_query\",\n            \"top_k\",\n            \"include_metadata\",\n            \"include_embeddings\",\n            \"metadata_filter\",\n        ],\n    }\n\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self._cached_kb_path: Path | None = None\n        self._knowledge_output_result: Data | DataFrame | None = None\n\n    def _pre_run_setup(self) -> None:\n        self._knowledge_output_result = None\n\n    async def _get_output_result(self, output: Output):\n        # Both legacy handles dispatch to the selected mode. Terminal nodes\n        # evaluate both handles, but must execute the storage operation once.\n        if output.method not in {\"build_kb_info\", \"retrieve_data\"}:\n            return await super()._get_output_result(output)\n        if self._knowledge_output_result is None:\n            raw_output = output.model_copy(update={\"options\": None})\n            self._knowledge_output_result = await super()._get_output_result(raw_output)\n        output.value = output.apply_options(self._knowledge_output_result)\n        return output.value\n\n    @staticmethod\n    def _embedding_provider_from_selection(selection: Any) -> str | None:\n        \"\"\"Read a provider name from a raw or persisted model selection.\"\"\"\n        if isinstance(selection, list):\n            selection = selection[0] if selection else None\n        if not isinstance(selection, Mapping):\n            return None\n        provider = selection.get(\"provider\")\n        if not isinstance(provider, str) or not provider.strip():\n            return None\n        return provider.strip()\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Resolve the embedding provider before dialog hooks or runtime secrets.\n\n        New-KB configuration carries its ModelInput inside a dropdown dialog,\n        while existing KBs persist the provider in the KB row. Neither is a\n        top-level ModelInput, so both must be resolved explicitly before the\n        generic component pipeline hydrates ``api_key``.\n        \"\"\"\n        from lfx.base.models.provider_registry import resolve_provider_id\n        from lfx.services.model_provider_policy import ModelProviderPolicyError\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        knowledge_value = effective_parameters.get(\"knowledge_base\", getattr(self, \"knowledge_base\", None))\n\n        if isinstance(knowledge_value, Mapping):\n            if \"02_embedding_model\" not in knowledge_value:\n                return ()\n            provider = self._embedding_provider_from_selection(knowledge_value.get(\"02_embedding_model\"))\n        elif isinstance(knowledge_value, str) and knowledge_value.strip():\n            metadata = await self._get_kb_metadata(knowledge_value.strip())\n            provider = self._embedding_provider_from_selection(metadata.get(\"model_selection\"))\n            if provider is None:\n                legacy_provider = metadata.get(\"embedding_provider\")\n                provider = legacy_provider.strip() if isinstance(legacy_provider, str) else None\n                if provider == \"Unknown\":\n                    provider = None\n        else:\n            return ()\n\n        if provider is None:\n            unknown_provider = \"unknown\"\n            raise ModelProviderPolicyError(unknown_provider, purpose)\n        return (resolve_provider_id(provider),)\n\n    @dataclass\n    class NewKnowledgeBaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"name\": \"create_knowledge_base\",\n                        \"description\": \"Create new knowledge in OpenXFlow.\",\n                        \"display_name\": \"Create new Knowledge Base\",\n                        \"field_order\": [\n                            \"01_new_kb_name\",\n                            \"02_embedding_model\",\n                            \"03_knowledge_backend\",\n                        ],\n                        \"template\": {\n                            \"01_new_kb_name\": StrInput(\n                                name=\"new_kb_name\",\n                                display_name=\"Knowledge Name\",\n                                info=\"Name of the new knowledge to create.\",\n                                required=True,\n                            ),\n                            \"02_embedding_model\": ModelInput(\n                                name=\"embedding_model\",\n                                display_name=\"Choose Embedding Model\",\n                                info=(\n                                    \"Select the embedding model to use for this knowledge base. \"\n                                    \"OpenXFlow uses the configured credentials for that model provider.\"\n                                ),\n                                required=True,\n                                model_type=\"embedding\",\n                            ),\n                            \"03_knowledge_backend\": DBProviderInput(\n                                name=\"knowledge_backend\",\n                                display_name=\"DB Provider\",\n                                info=(\n                                    \"Select where this knowledge base stores vectors. \"\n                                    \"OpenSearch uses the global DB Providers settings.\"\n                                ),\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    # ------ Inputs --------------------------------------------------------\n    # ``knowledge_base`` is kept at position 0 so legacy tests that check\n    # ``component.inputs[0].dialog_inputs`` continue to work.\n    inputs = [\n        DropdownInput(\n            name=\"knowledge_base\",\n            display_name=\"Knowledge\",\n            info=\"Select the knowledge to load data from.\",\n            required=True,\n            options=[],\n            refresh_button=True,\n            real_time_refresh=True,\n            dialog_inputs=asdict(NewKnowledgeBaseInput()),\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[MODE_INGEST, MODE_RETRIEVE],\n            value=MODE_INGEST,\n            info=\"Switch between writing new data into the knowledge base and querying it.\",\n            real_time_refresh=True,\n            tool_mode=True,\n        ),\n        # --- Ingest-only inputs (default-shown; hidden when mode == Retrieve) -\n        HandleInput(\n            name=\"input_df\",\n            display_name=\"Input\",\n            info=(\n                \"Table with all original columns (already chunked / processed). \"\n                \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n                \"it is converted to a DataFrame automatically.\"\n            ),\n            input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n            required=True,\n            dynamic=True,\n            show=True,\n        ),\n        TableInput(\n            name=\"column_config\",\n            display_name=\"Column Configuration\",\n            info=\"Configure column behavior for the knowledge base.\",\n            required=True,\n            table_schema=[\n                {\n                    \"name\": \"column_name\",\n                    \"display_name\": \"Column Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Name of the column in the source DataFrame\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"vectorize\",\n                    \"display_name\": \"Vectorize\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Create embeddings for this column\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"identifier\",\n                    \"display_name\": \"Identifier\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Use this column as unique identifier\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"column_name\": \"text\",\n                    \"vectorize\": True,\n                    \"identifier\": True,\n                },\n            ],\n            dynamic=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"Batch size for processing embeddings\",\n            advanced=True,\n            value=1000,\n            dynamic=True,\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Embedding Provider API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        BoolInput(\n            name=\"allow_duplicates\",\n            display_name=\"Allow Duplicates\",\n            info=\"Allow duplicate rows in the knowledge base\",\n            advanced=True,\n            value=False,\n            dynamic=True,\n            show=True,\n        ),\n        StrInput(\n            name=\"metadata_json\",\n            display_name=\"Metadata\",\n            info=(\n                \"Optional JSON object of user metadata applied to every chunk produced by this \"\n                'run (e.g. {\"tag\": \"invoice\", \"year\": \"2026\"}). Same shape as the upload modal '\n                \"Metadata section so chunks browser filters + Knowledge retrieval metadata_filter \"\n                \"work uniformly across upload, folder, and flow-driven ingestion. Malformed JSON is \"\n                \"ignored with a warning rather than failing the run.\"\n            ),\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        # --- Retrieve-only inputs (default-hidden; shown when mode == Retrieve) -\n        MessageTextInput(\n            name=\"search_query\",\n            display_name=\"Search Query\",\n            info=\"Optional search query to filter knowledge base data.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of top results to return from the knowledge base.\",\n            value=5,\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_metadata\",\n            display_name=\"Include Metadata\",\n            info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n            value=True,\n            advanced=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_embeddings\",\n            display_name=\"Include Embeddings\",\n            info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n            value=False,\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"metadata_filter\",\n            display_name=\"Metadata Filter\",\n            info=(\n                \"Optional JSON object of user-metadata key/value pairs. Only chunks \"\n                'whose source_metadata matches every key are returned (e.g. {\"tag\": \"invoice\"} '\n                'or {\"tag\": [\"invoice\", \"audit\"]} for OR-of-values). Backends without '\n                \"native filtering apply the match client-side after retrieval.\"\n            ),\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n    ]\n\n    # ------ Outputs -------------------------------------------------------\n    # Both outputs are declared at the class level so the runtime can\n    # dispatch to either method depending on which one the saved flow has\n    # wired up. ``update_outputs`` filters the canvas-visible output per\n    # selected mode; see ``TypeConverterComponent`` and ``MemoryComponent``\n    # for the same pattern.\n    #\n    # Output names match the legacy ``KnowledgeIngestionComponent``\n    # (``dataframe_output``) and ``KnowledgeBaseComponent``\n    # (``retrieve_data``) so saved flow edges keyed on those names resolve\n    # cleanly against the merged component.\n    outputs = [\n        Output(\n            display_name=\"Results\",\n            name=\"dataframe_output\",\n            method=\"build_kb_info\",\n            types=[\"JSON\"],\n            selected=\"JSON\",\n        ),\n        Output(\n            display_name=\"Results\",\n            name=\"retrieve_data\",\n            method=\"retrieve_data\",\n            info=\"Returns the data from the selected knowledge base.\",\n            types=[\"Table\"],\n            selected=\"Table\",\n        ),\n    ]\n\n    # ------ Mode-driven UI updates ---------------------------------------\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"Sync the visible output with the current ``mode`` value on canvas load.\n\n        Saved flows hit this path: ``update_outputs`` is normally only triggered\n        by ``real_time_refresh`` field edits, so without this re-sync the\n        canvas could land with both outputs visible.\n        \"\"\"\n        await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = new_frontend_node.get(\"template\", {})\n        mode_value = template.get(\"mode\", {}).get(\"value\", MODE_INGEST)\n        self.update_outputs(new_frontend_node, \"mode\", mode_value)\n        # The rebuilt template carries class-default (Ingest) ``show`` flags,\n        # so a saved Retrieve node returned ``search_query`` hidden and the canvas\n        # silently dropped the edge feeding it.\n        self._apply_mode_visibility(template)\n        return new_frontend_node\n\n    def _apply_mode_visibility(self, build_config, field_name: str | None = None, field_value: Any = None):\n        \"\"\"Show only the inputs that belong to the effective mode.\"\"\"\n        current_mode = build_config.get(\"mode\", {}).get(\"value\") if isinstance(build_config, dict) else None\n        if field_name == \"mode\":\n            current_mode = field_value\n        # Map legacy/emoji-prefixed labels onto the current canonical values so\n        # flows saved before the label change still toggle visibility correctly.\n        if _is_retrieve_mode(current_mode):\n            current_mode = MODE_RETRIEVE\n        elif current_mode not in self.mode_config:\n            current_mode = MODE_INGEST\n        return set_current_fields(\n            build_config=build_config if isinstance(build_config, dotdict) else dotdict(build_config),\n            action_fields=self.mode_config,\n            selected_action=current_mode,\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Filter visible outputs to match the selected mode.\n\n        Triggered by the ``mode`` ``TabInput`` ``real_time_refresh`` flag.\n        The class-level ``outputs`` list always carries both entries so the\n        runtime can resolve either ``build_kb_info`` or ``retrieve_data``\n        regardless of canvas visibility — we just hide the unused one here.\n        \"\"\"\n        if field_name != \"mode\":\n            return frontend_node\n        if _is_retrieve_mode(field_value):\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"retrieve_data\",\n                    method=\"retrieve_data\",\n                    info=\"Returns the data from the selected knowledge base.\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                )\n            ]\n        else:\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"dataframe_output\",\n                    method=\"build_kb_info\",\n                    types=[\"JSON\"],\n                    selected=\"JSON\",\n                )\n            ]\n        return frontend_node\n\n    async def update_build_config(\n        self,\n        build_config,\n        field_value: Any,\n        field_name: str | None = None,\n    ):\n        \"\"\"Refresh KB options, drive the create-KB dialog, and hide off-mode fields.\"\"\"\n        # Astra-cloud gate covers both ingest and retrieve paths.\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        # Always populate the create-KB dialog's embedding-model options so the\n        # ModelInput renders correctly regardless of which input triggered the\n        # refresh.\n        try:\n            dialog_template = (\n                build_config[\"knowledge_base\"]\n                .get(\"dialog_inputs\", {})\n                .get(\"fields\", {})\n                .get(\"data\", {})\n                .get(\"node\", {})\n                .get(\"template\", {})\n            )\n            if \"02_embedding_model\" in dialog_template:\n                embedding_options = get_embedding_model_options(user_id=self.user_id)\n                dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n        except Exception:  # noqa: BLE001\n            self.log(\"Failed to populate embedding model options in dialog\")\n\n        # KB-picker refresh + create-new-KB flow (lifted from the legacy ingestion\n        # component verbatim; relied on by both the canvas refresh button and the\n        # dialog-submit path).\n        if field_name == \"knowledge_base\":\n            # Lazy import keeps lfx importable without langflow installed.\n            from langflow.services.database.models.user.crud import get_user_by_id\n\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for fetching knowledge base list.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n                if not current_user:\n                    msg = f\"User with ID {self.user_id} not found.\"\n                    raise ValueError(msg)\n            if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n                model_selection = field_value[\"02_embedding_model\"]\n                if isinstance(model_selection, dict):\n                    model_selection = [model_selection]\n\n                backend_type, backend_config = self._normalize_backend_selection(\n                    field_value.get(\"03_knowledge_backend\")\n                )\n                ensure_storage_routing_allowed(backend_config, is_superuser=bool(current_user.is_superuser))\n                new_kb_name = field_value[\"01_new_kb_name\"]\n                from langflow.api.utils.kb_helpers import local_chroma_rejection_reason, validate_kb_name\n\n                validate_kb_name(new_kb_name)\n                rejection = local_chroma_rejection_reason(backend_type, backend_config)\n                if rejection:\n                    raise ValueError(rejection)\n\n                embed_model = get_embeddings(\n                    model=model_selection,\n                    user_id=self.user_id,\n                )\n\n                try:\n                    await asyncio.wait_for(\n                        asyncio.to_thread(embed_model.embed_query, \"test\"),\n                        timeout=10,\n                    )\n                except TimeoutError as e:\n                    msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n                    raise ValueError(msg) from e\n                except Exception as e:\n                    msg = f\"Embedding validation failed: {e!s}\"\n                    raise ValueError(msg) from e\n\n                build_config[\"knowledge_base\"][\"value\"] = new_kb_name\n                await self._create_knowledge_base_record(\n                    user_id=self.user_id,\n                    name=field_value[\"01_new_kb_name\"],\n                    model_selection=model_selection,\n                    backend_type=backend_type,\n                    backend_config=backend_config,\n                )\n\n            build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(user_id=self.user_id)\n            if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n                build_config[\"knowledge_base\"][\"value\"] = None\n\n        # Honor the current mode regardless of which field triggered the refresh.\n        return self._apply_mode_visibility(build_config, field_name, field_value)\n\n    # =====================================================================\n    #                       INGESTION CODE PATH\n    # =====================================================================\n    @staticmethod\n    def _scalar_notna(value) -> bool:\n        \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n        ``pd.notna`` returns an array when given an array-like input, which\n        cannot be used directly in a boolean context.  This helper collapses\n        the result to a single scalar ``bool``.\n        \"\"\"\n        result = pd.notna(value)\n        if hasattr(result, \"__iter__\") and not isinstance(result, str):\n            import numpy as np\n\n            arr = np.asarray(result)\n            return arr.size > 0 and arr.all()\n        return bool(result)\n\n    def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n        \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n        if not self.column_config:\n            msg = \"Column configuration cannot be empty\"\n            raise ValueError(msg)\n\n        config_list = self.column_config if isinstance(self.column_config, list) else []\n\n        df_columns = set(df_source.columns)\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            if col_name not in df_columns:\n                msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n                raise ValueError(msg)\n\n        return config_list\n\n    @staticmethod\n    def _extract_source_types_from_df(df_source: pd.DataFrame) -> set[str]:\n        \"\"\"Pull file extensions out of common path/name columns on the source DataFrame.\n\n        The direct-upload ingestion path stores extensions in\n        the KB row's ``source_types`` so the KB list can render the\n        correct file-type icon. When ingestion happens via a connected\n        ``input_df`` (e.g. File → Knowledge) the same field stayed empty and\n        the icon defaulted to a blank tile. We look at the well-known columns\n        the File / S3 / cloud-storage components produce and collect any\n        plausible extension so the icon is consistent across both flows.\n        \"\"\"\n        candidate_columns = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for col in candidate_columns:\n            if col not in df_source.columns:\n                continue\n            for value in df_source[col].dropna():\n                ext = KnowledgeComponent._extension_from_value(value)\n                # Drop anything that doesn't look like an extension (URL\n                # query strings, version segments, etc.) — the icon palette\n                # keys off short alphanumeric tokens like \"pdf\"/\"docx\".\n                if ext:\n                    extensions.add(ext)\n        return extensions\n\n    @staticmethod\n    def _extension_from_value(value: Any) -> str | None:\n        \"\"\"Return a normalized file-extension token from a path / filename / MIME string.\n\n        Accepts values like ``\"report.PDF\"``, ``\"/docs/notes.txt\"``, or\n        ``\"application/pdf\"`` and returns ``\"pdf\"`` / ``\"txt\"``. Returns\n        ``None`` if no plausible extension can be derived.\n        \"\"\"\n        extension_length_limit = 10\n        if value is None:\n            return None\n        text = str(value).strip()\n        if not text:\n            return None\n        # MIME types like ``application/pdf`` carry the canonical extension\n        # in the subtype slot — preserve them so File / S3 messages keyed\n        # only on ``mimetype`` still resolve to an icon.\n        if \"/\" in text and \".\" not in text.rsplit(\"/\", 1)[-1]:\n            subtype = text.rsplit(\"/\", 1)[-1].strip().lower()\n            return subtype if subtype and len(subtype) <= extension_length_limit and subtype.isalnum() else None\n        if \".\" not in text:\n            return None\n        ext = text.rsplit(\".\", 1)[-1].strip().lower()\n        if ext and len(ext) <= extension_length_limit and ext.isalnum():\n            return ext\n        return None\n\n    @classmethod\n    def _extract_source_types_from_mapping(cls, mapping: Any) -> set[str]:\n        \"\"\"Pull extensions from a Message/Data-style ``data`` dict or a plain dict.\"\"\"\n        if not isinstance(mapping, dict):\n            return set()\n        candidate_keys = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for key in candidate_keys:\n            ext = cls._extension_from_value(mapping.get(key))\n            if ext:\n                extensions.add(ext)\n        return extensions\n\n    @classmethod\n    def _extract_source_types_from_input(cls, input_value: Any) -> set[str]:\n        \"\"\"Pull file extensions out of the raw component input.\n\n        ``convert_to_dataframe`` strips Message/Data fields down to ``text``\n        when projecting onto a DataFrame, so file metadata attached to a\n        File-component \"Raw Content\" output never reaches\n        ``_extract_source_types_from_df``. Looking at the raw input first\n        keeps the KB icon consistent with direct upload.\n        \"\"\"\n        if input_value is None:\n            return set()\n        if isinstance(input_value, list):\n            extensions: set[str] = set()\n            for item in input_value:\n                extensions |= cls._extract_source_types_from_input(item)\n            return extensions\n        if isinstance(input_value, pd.DataFrame):\n            return cls._extract_source_types_from_df(input_value)\n        # Message / Data / JSON all expose a ``data`` dict via the lfx schema.\n        mapping = getattr(input_value, \"data\", None)\n        if mapping is None and isinstance(input_value, dict):\n            mapping = input_value\n        return cls._extract_source_types_from_mapping(mapping)\n\n    async def _refresh_kb_stats(\n        self,\n        *,\n        kb_record_id: Any,\n        backend: BaseVectorStoreBackend,\n        extensions: set[str],\n    ) -> None:\n        \"\"\"Recount the KB from its vector store and write the totals onto the row.\n\n        Backend-agnostic: counts come from ``count`` / ``iter_documents`` /\n        ``storage_size_bytes``, so a Chroma, OpenSearch, or pgVector KB all report\n        real numbers. ``extensions`` are merged into ``source_types`` so the KB\n        list renders the right file-type icon regardless of which ingestion route\n        produced the chunks.\n\n        Best-effort: the chunks are already written and committed by the time this\n        runs, so a stats refresh failure must not fail the ingestion.\n        \"\"\"\n        if kb_record_id is None:\n            return\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return\n\n        try:\n            chunks = await backend.count()\n            characters = 0\n            words = 0\n            async with aclosing(backend.iter_documents()) as batches:\n                async for batch in batches:\n                    for document in batch:\n                        characters += len(document.content)\n                        words += len(document.content.split())\n\n            record = await knowledge_base_service.get_by_id(kb_record_id)\n            existing = set(record.source_types or []) if record is not None else set()\n\n            await knowledge_base_service.update_stats(\n                kb_record_id,\n                chunks=chunks,\n                words=words,\n                characters=characters,\n                size_bytes=await backend.storage_size_bytes(),\n                source_types=sorted(existing | extensions),\n            )\n        except Exception as e:  # noqa: BLE001 — stats are cosmetic; chunks are already written\n            self.log(f\"Warning: Could not refresh knowledge base stats: {e}\")\n\n    @staticmethod\n    def _default_backend_selection() -> tuple[str, dict[str, Any]]:\n        \"\"\"Deployment default for a new KB with no explicit backend chosen.\n\n        Environment-driven: pgVector when ``PGVECTOR_CONNECTION_STRING`` is set,\n        else OpenSearch when ``OPENSEARCH_URL`` is set, else SQLite. An\n        unspecified selection (headless flows, multi-replica) therefore lands on\n        the deployment's shared store.\n        \"\"\"\n        from lfx.base.knowledge_bases.backends.postgres import resolve_default_kb_backend\n\n        backend_type = resolve_default_kb_backend()\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, dict(_DEFAULT_OPENSEARCH_CONFIG)\n        return backend_type, {}\n\n    @classmethod\n    def _normalize_backend_selection(cls, value: Any) -> tuple[str, dict[str, Any]]:\n        \"\"\"Normalize a DBProviderInput value into backend type/config.\"\"\"\n        if not value:\n            return cls._default_backend_selection()\n\n        backend_config: dict[str, Any]\n        if isinstance(value, str):\n            backend_type, backend_config = value, {}\n        elif isinstance(value, dict):\n            backend_type = str(value.get(\"backend_type\") or value.get(\"id\") or BackendType.SQLITE.value)\n            backend_config = value.get(\"backend_config\") or value.get(\"config\") or {}\n        else:\n            msg = \"Invalid vector-store backend selection.\"\n            raise TypeError(msg)\n        if not isinstance(backend_config, dict):\n            msg = \"Vector-store backend configuration must be an object.\"\n            raise TypeError(msg)\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, {**_DEFAULT_OPENSEARCH_CONFIG, **backend_config}\n        if backend_type in {BackendType.SQLITE.value, BackendType.POSTGRES.value}:\n            return backend_type, backend_config\n        msg = \"Chroma is retired. Choose SQLite, pgVector, or OpenSearch for a new knowledge base.\"\n        raise ValueError(msg)\n\n    async def _create_knowledge_base_record(\n        self,\n        *,\n        user_id: Any,\n        name: str,\n        model_selection: list[dict[str, Any]],\n        backend_type: str,\n        backend_config: dict[str, Any],\n    ) -> None:\n        \"\"\"Persist the component-created KB in the DB when OpenXFlow is available.\"\"\"\n        from langflow.api.utils import knowledge_base_service\n\n        await knowledge_base_service.create_record(\n            user_id=user_id,\n            name=name,\n            model_selection=model_selection,\n            column_config=_normalize_column_config(self.column_config) if isinstance(self.column_config, list) else [],\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n        \"\"\"Build detailed column metadata.\"\"\"\n        metadata: dict[str, Any] = {\n            \"total_columns\": len(df_source.columns),\n            \"mapped_columns\": len(config_list),\n            \"unmapped_columns\": len(df_source.columns) - len(config_list),\n            \"columns\": [],\n            \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n        }\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            vectorize = coalesce_bool(config.get(\"vectorize\"))\n            identifier = coalesce_bool(config.get(\"identifier\"))\n\n            metadata[\"columns\"].append(\n                {\n                    \"name\": col_name,\n                    \"vectorize\": vectorize,\n                    \"identifier\": identifier,\n                }\n            )\n\n            if vectorize:\n                metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n            if identifier:\n                metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n        return metadata\n\n    async def _create_vector_store(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        \"\"\"Create vector store using the configured DB provider.\"\"\"\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            return await self._ingest_into_backend(backend, df_source, config_list, embedding_function, job_id=job_id)\n        except BaseException:\n            await _finish_despite_cancellation(backend.teardown())\n            raise\n\n    async def _ingest_into_backend(\n        self,\n        backend: BaseVectorStoreBackend,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        await backend.ensure_ready()\n\n        from langflow.services.knowledge_base_storage.runtime import operation, resolve_record\n\n        record = await resolve_record(self._user_uuid, self.knowledge_base)\n        async with operation(record, shared=True):\n            existing_ids = set()\n            if not self.allow_duplicates:\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        for document in batch:\n                            doc_id = document.metadata.get(\"_id\")\n                            if doc_id:\n                                existing_ids.add(doc_id)\n\n        data_objects = await self._convert_df_to_data_objects(df_source, config_list, existing_ids=existing_ids)\n\n        user_metadata_tag = self._resolve_user_metadata_tag()\n\n        documents = []\n        for data_obj in data_objects:\n            doc = data_obj.to_lc_document()\n            if user_metadata_tag:\n                doc.metadata[\"source_metadata\"] = user_metadata_tag\n            documents.append(doc)\n        if not documents:\n            return backend\n\n        # No storage lease may surround the provider call. The write lease\n        # below rechecks routing and IDs after concurrent operations finish.\n        vectors = await self._embed_unless_job_cancelled(\n            embedding_function, [doc.page_content for doc in documents], job_id\n        )\n        if len(vectors) != len(documents):\n            msg = \"Embedding provider returned an incorrect number of vectors\"\n            raise BackendConfigurationError(msg)\n        embedded = [\n            IngestedDocument(doc.page_content, doc.metadata, vector, id=doc.id)\n            for doc, vector in zip(documents, vectors, strict=True)\n        ]\n        async with operation(record):\n            # A cancel that landed after the embeddings returned must still\n            # keep this run's chunks out of the KB.\n            await self._raise_if_job_cancelled(job_id)\n            if not self.allow_duplicates:\n                current_ids = set()\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        current_ids.update(doc.metadata[\"_id\"] for doc in batch if doc.metadata.get(\"_id\"))\n                unique = []\n                for doc in embedded:\n                    doc_id = doc.metadata.get(\"_id\")\n                    if doc_id and doc_id in current_ids:\n                        continue\n                    if doc_id:\n                        current_ids.add(doc_id)\n                    unique.append(doc)\n                embedded = unique\n\n            if embedded:\n                await backend.add_embedded_documents(embedded)\n                self.log(f\"Added {len(embedded)} documents to vector store '{self.knowledge_base}'\")\n\n        return backend\n\n    async def _embed_unless_job_cancelled(\n        self, embedding_function, texts: list[str], job_id: uuid.UUID | None\n    ) -> list[list[float]]:\n        \"\"\"Embed ``texts``, giving up as soon as the run's job is cancelled.\n\n        The Knowledge page cancels a run by marking its job. The embedding call\n        is the long, write-free part of a flow ingestion, so it is the safe\n        place to stop.\n        \"\"\"\n        await self._raise_if_job_cancelled(job_id)\n        if job_id is None:\n            return await embedding_function.aembed_documents(texts)\n\n        # A task group would wrap provider errors in an ExceptionGroup, so a\n        # plain watcher task cancels this scope instead.\n        with anyio.CancelScope() as scope:\n            watcher = asyncio.ensure_future(self._cancel_when_job_cancelled(job_id, scope))\n            try:\n                return await embedding_function.aembed_documents(texts)\n            finally:\n                # The watcher may be mid-poll; let its DB session close.\n                watcher.cancel()\n                with anyio.CancelScope(shield=True):\n                    await asyncio.wait({watcher})\n        raise self._cancelled_error()\n\n    async def _cancel_when_job_cancelled(self, job_id: uuid.UUID, scope: anyio.CancelScope) -> None:\n        # The cancel is a job-row write from another request, so it is polled.\n        while True:\n            await asyncio.sleep(JOB_CANCEL_POLL_SECONDS)\n            if await self._job_cancelled(job_id):\n                scope.cancel()\n                return\n\n    async def _raise_if_job_cancelled(self, job_id: uuid.UUID | None) -> None:\n        if await self._job_cancelled(job_id):\n            raise self._cancelled_error()\n\n    def _cancelled_error(self) -> KnowledgeIngestionCancelledError:\n        return KnowledgeIngestionCancelledError(f\"Ingestion into '{self.knowledge_base}' was cancelled.\")\n\n    async def _job_cancelled(self, job_id: uuid.UUID | None) -> bool:\n        if job_id is None:\n            return False\n        try:\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError:\n            return False\n        try:\n            job = await get_job_service().get_job_by_job_id(job_id)\n        except Exception as exc:  # noqa: BLE001 — a failed poll must not abort the ingestion\n            self.log(f\"Could not check whether ingestion job {job_id} was cancelled: {exc}\")\n            return False\n        return job is not None and job.status == JobStatus.CANCELLED\n\n    async def _convert_df_to_data_objects(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        existing_ids: set[str] | None = None,\n    ) -> list[Data]:\n        \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n        data_objects: list[Data] = []\n\n        if existing_ids is None:\n            existing_ids = set()\n\n        content_cols = []\n        identifier_cols = []\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n\n            if coalesce_bool(config.get(\"vectorize\")):\n                content_cols.append(col_name)\n            if coalesce_bool(config.get(\"identifier\")):\n                identifier_cols.append(col_name)\n\n        for _, row in df_source.iterrows():\n            identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n            page_content = \" \".join(identifier_parts)\n\n            data_dict = {\n                \"text\": page_content,\n            }\n\n            if identifier_cols:\n                identifier_parts = [\n                    str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n                ]\n                page_content = \" \".join(identifier_parts)\n\n            for col in df_source.columns:\n                if col not in content_cols and col in row and self._scalar_notna(row[col]):\n                    value = row[col]\n                    data_dict[col] = str(value)\n\n            page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n            data_dict[\"_id\"] = page_content_hash\n\n            if not self.allow_duplicates and page_content_hash in existing_ids:\n                self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n                continue\n\n            data_obj = Data(data=data_dict)\n            data_objects.append(data_obj)\n\n        return data_objects\n\n    def is_valid_collection_name(self, name: str) -> bool:\n        \"\"\"Return whether ``name`` satisfies the shared collection-name contract.\"\"\"\n        return is_valid_kb_collection_name(name)\n\n    def _resolve_store_path(\n        self,\n        kb_user: str,\n        backend_type: str,\n        backend_config: dict[str, Any] | None,\n    ) -> Path | None:\n        \"\"\"Return this KB's on-disk directory, or ``None`` when it needs no disk.\n\n        Only local Chroma stores anything on this box; every other backend\n        resolves to ``None`` and never touches the filesystem.\n        \"\"\"\n        # Lazy import keeps lfx importable without langflow's DB services.\n        from langflow.api.utils.kb_helpers import resolve_local_store_path\n\n        return resolve_local_store_path(\n            self.knowledge_base,\n            kb_user,\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    async def _kb_username(self) -> str:\n        \"\"\"Resolve the owning user's username (used to root a local-Chroma path).\"\"\"\n        cached = getattr(self, \"_cached_kb_username\", None)\n        if cached is not None:\n            return cached\n\n        # Lazy import to keep ``lfx`` importable standalone — langflow's\n        # user/DB models are not always available at module load time.\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        async with session_scope() as db:\n            if not self.user_id:\n                msg = \"User ID is required for fetching knowledge base path.\"\n                raise ValueError(msg)\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n            self._cached_kb_username = current_user.username\n\n        return self._cached_kb_username\n\n    async def _kb_path(self) -> Path | None:\n        \"\"\"Resolve the KB's local directory from its configured backend.\"\"\"\n        cached_path = getattr(self, \"_cached_kb_path\", None)\n        if cached_path is not None:\n            return cached_path\n\n        backend_type, backend_config = await self._resolve_backend_config()\n        kb_user = await self._kb_username()\n        self._cached_kb_path = self._resolve_store_path(kb_user, backend_type, backend_config)\n        return self._cached_kb_path\n\n    def _resolve_user_metadata_tag(self) -> str:\n        \"\"\"Return the JSON-encoded user metadata tag for chunk writes.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return \"\"\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return \"\"\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError) as exc:\n            self.log(f\"KnowledgeComponent: metadata_json is not valid JSON ({exc}); skipping metadata stamp.\")\n            return \"\"\n        if not isinstance(decoded, dict):\n            self.log(\"KnowledgeComponent: metadata_json must decode to a JSON object; skipping metadata stamp.\")\n            return \"\"\n        return json.dumps(decoded, sort_keys=True)\n\n    async def build_kb_info(self) -> Data:\n        \"\"\"Main ingestion routine → returns a dict with KB metadata.\n\n        The annotation is intentionally narrowed to ``Data`` even though the\n        cross-mode fallback below may return a ``DataFrame`` from\n        ``retrieve_data``. The frontend builds React-Flow handle IDs from\n        this output's type list; widening it to ``Data | DataFrame`` makes\n        the API advertise ``[\"JSON\", \"Table\"]`` for the ingest output and\n        breaks every saved-edge sourceHandle that was generated against\n        a single-type handle (BUG-02). Python doesn't enforce return\n        annotations at runtime, so the rare fallback path keeps working.\n        \"\"\"\n        if _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.retrieve_data()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        run_id: uuid.UUID | None = None\n        run_job_id: uuid.UUID | None = None\n        run_summary: IngestionSummary | None = None\n        run_status: IngestionRunStatus = IngestionRunStatus.SUCCEEDED\n        run_error: str | None = None\n        kb_record_id: uuid.UUID | None = None\n        previous_kb_status = \"ready\"\n        previous_failure_reason: str | None = None\n        try:\n            input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n            df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n            config_list = self._validate_column_config(df_source)\n            column_metadata = self._build_column_metadata(config_list, df_source)\n\n            # Embedding config comes from the ``knowledge_base`` row — the sole\n            # authority. No sidecar is read, so ingestion resolves the same model\n            # on any replica.\n            stored_metadata = await self._get_kb_metadata()\n            # A cancelled run restores this status. Only an earlier failure is\n            # kept; \"empty\" is an API projection of a ready KB with no chunks.\n            if stored_metadata.get(\"status\") == \"failed\":\n                previous_kb_status = \"failed\"\n                previous_failure_reason = stored_metadata.get(\"failure_reason\")\n            model_selection = stored_metadata.get(\"model_selection\")\n            if model_selection:\n                model_selection = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            else:\n                embedding_model_name = stored_metadata.get(\"embedding_model\")\n                embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n                if embedding_model_name:\n                    try:\n                        all_options = get_embedding_model_options(user_id=self.user_id)\n                        match = next(\n                            (o for o in all_options if o.get(\"name\") == embedding_model_name),\n                            None,\n                        )\n                        if match:\n                            model_selection = [match]\n                        else:\n                            self.log(\n                                f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n                                \"from the knowledge base record is no longer available in the model registry. \"\n                                \"Please re-create this knowledge base with a supported embedding model.\"\n                            )\n                            msg = (\n                                f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n                                \"The knowledge base was created with an older format and the model \"\n                                \"is not available in the current registry. \"\n                                \"Please re-create the knowledge base with a supported embedding model.\"\n                            )\n                            raise ValueError(msg)\n                    except ValueError:\n                        raise\n                    except Exception:  # noqa: BLE001\n                        self.log(\n                            f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n                            \"Please re-create this knowledge base with a supported embedding model.\"\n                        )\n                        msg = (\n                            f\"Could not look up embedding model '{embedding_model_name}' \"\n                            f\"(provider: {embedding_provider}). \"\n                            \"Please re-create the knowledge base with a supported embedding model.\"\n                        )\n                        raise ValueError(msg)  # noqa: B904\n\n            if not model_selection:\n                msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n                raise ValueError(msg)\n\n            # ``api_key=None`` falls through to the user's variables table and\n            # then the environment inside ``get_embeddings`` — the same\n            # resolution the server-side ingestion path uses. The component input\n            # only overrides it.\n            embedding_function = get_embeddings(\n                model=model_selection,\n                user_id=self.user_id,\n                api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n                chunk_size=self.chunk_size,\n            )\n\n            kb_path = await self._kb_path()\n\n            run_id, run_job_id, run_summary, kb_record_id = await self._begin_ingestion_run(kb_path)\n            if kb_record_id is not None:\n                await self._record_kb_status(kb_record_id, \"ingesting\")\n\n            backend = await self._create_vector_store(\n                df_source, config_list, embedding_function=embedding_function, job_id=run_job_id\n            )\n\n            try:\n                # Stamp the KB with the file extensions we just ingested so\n                # the Knowledge Bases list renders the correct icon for\n                # flow-driven ingestion (input_df), matching direct upload.\n                # We look at the raw input first because ``convert_to_dataframe``\n                # drops Message/Data metadata fields (file_path, mimetype, …)\n                # when projecting onto the DataFrame.\n                source_types = self._extract_source_types_from_input(input_value)\n                source_types |= self._extract_source_types_from_df(df_source)\n                # Storage routing returns a guarded proxy with the backend's\n                # methods, so a concrete-class check would skip the refresh.\n                await self._refresh_kb_stats(\n                    kb_record_id=kb_record_id,\n                    backend=backend,\n                    extensions=source_types,\n                )\n            finally:\n                await _finish_despite_cancellation(backend.teardown())\n\n            meta: dict[str, Any] = {\n                \"kb_id\": str(uuid.uuid4()),\n                \"kb_name\": self.knowledge_base,\n                \"rows\": len(df_source),\n                \"column_metadata\": column_metadata,\n                \"path\": str(kb_path),\n                \"config_columns\": len(config_list),\n                \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n            }\n\n            if run_summary is not None:\n                run_summary.record_item(\n                    IngestionItemResult(\n                        item_id=self.knowledge_base,\n                        display_name=f\"{self.knowledge_base} ({len(df_source)} rows)\",\n                        status=IngestionItemStatus.SUCCEEDED,\n                        chunks_created=len(df_source),\n                    ),\n                )\n\n            if kb_record_id is not None:\n                # Stats were already refreshed straight from the backend above.\n                await self._record_kb_status(kb_record_id, \"ready\")\n\n            self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n            return Data(data=meta)\n\n        except asyncio.CancelledError:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = \"Flow ingestion was cancelled.\"\n            raise\n        except KnowledgeIngestionCancelledError as e:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = str(e)\n            raise\n        except (OSError, ValueError, RuntimeError, KeyError) as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            msg = f\"Error during KB ingestion: {e}\"\n            raise RuntimeError(msg) from e\n        except Exception as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            raise\n        finally:\n\n            async def settle_run() -> None:\n                if run_id is not None and run_summary is not None:\n                    await self._finalize_ingestion_run(\n                        run_id=run_id,\n                        job_id=run_job_id,\n                        summary=run_summary,\n                        status=run_status,\n                        error_message=run_error,\n                    )\n                if kb_record_id is not None:\n                    if run_status is IngestionRunStatus.FAILED:\n                        await self._record_kb_status(kb_record_id, \"failed\", failure_reason=run_error)\n                    elif run_status is IngestionRunStatus.CANCELLED:\n                        await self._record_kb_status(\n                            kb_record_id, previous_kb_status, failure_reason=previous_failure_reason\n                        )\n\n            # A stopped build can cancel this task more than once, and the\n            # writes that make the run terminal must survive every cancel.\n            await _finish_despite_cancellation(settle_run())\n\n    async def _begin_ingestion_run(\n        self,\n        kb_path: Path,\n    ) -> tuple[uuid.UUID | None, uuid.UUID | None, IngestionSummary | None, uuid.UUID | None]:\n        \"\"\"Create a parent ``Job`` and seed an ingestion-run row.\"\"\"\n        if not self.user_id:\n            self.log(\"No user_id on component; skipping ingestion-run tracking.\")\n            return None, None, None, None\n\n        try:\n            user_uuid = uuid.UUID(str(self.user_id))\n        except (ValueError, TypeError) as exc:\n            self.log(f\"Could not coerce user_id={self.user_id!r} to UUID; skipping run tracking ({exc}).\")\n            return None, None, None, None\n\n        try:\n            from langflow.api.utils import ingestion_run_service, knowledge_base_service\n            from langflow.services.database.models.jobs.model import JobStatus, JobType\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion will not be recorded ({exc}).\")\n            return None, None, None, None\n\n        job_id: uuid.UUID | None = None\n        run_id: uuid.UUID | None = None\n        summary: IngestionSummary | None = None\n        try:\n            kb_record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n            kb_record_id = kb_record.id if kb_record is not None else None\n\n            user_metadata = self._parse_user_metadata_dict()\n\n            job_id = uuid.uuid4()\n            job_service = get_job_service()\n            raw_flow_id = getattr(self, \"flow_id\", None)\n            flow_id_uuid = uuid.UUID(str(raw_flow_id)) if raw_flow_id else job_id\n            source = FlowComponentSource(\n                user_id=user_uuid,\n                source_config={\n                    \"knowledge_base\": self.knowledge_base,\n                    \"kb_path\": str(kb_path),\n                    \"flow_id\": str(flow_id_uuid),\n                },\n            )\n            summary = IngestionSummary(\n                kb_name=self.knowledge_base,\n                source_type=source.source_type.value,\n                user_id=user_uuid,\n                job_id=job_id,\n                source_config=source.describe().get(\"config\") or {},\n                user_metadata=user_metadata,\n            )\n            # Inserted running and already heartbeated, so an orphan sweep in\n            # another worker never sees this live run without a heartbeat.\n            await job_service.create_job(\n                job_id=job_id,\n                flow_id=flow_id_uuid,\n                job_type=JobType.INGESTION,\n                asset_id=kb_record_id,\n                asset_type=\"knowledge_base\",\n                user_id=user_uuid,\n                status=JobStatus.IN_PROGRESS,\n                heartbeat=True,\n            )\n\n            run_id = await ingestion_run_service.create_run(\n                kb_name=self.knowledge_base,\n                source=source,\n                job_id=job_id,\n                user_id=user_uuid,\n                kb_id=kb_record_id,\n                user_metadata=user_metadata,\n            )\n            if run_id is None:\n                return None, job_id, None, kb_record_id\n            await ingestion_run_service.mark_running(run_id)\n\n            self.log(f\"Started ingestion run job_id={job_id} kb_name={self.knowledge_base} kb_id={kb_record_id}\")\n            # Keeps the job heartbeated until _finalize_ingestion_run stops it.\n            await job_service.start_keep_alive(job_id)\n        except asyncio.CancelledError:\n            # The caller has not received the run ID yet, so it cannot finalize\n            # a cancellation that arrives while tracking is being initialized.\n            if job_id is not None and summary is not None:\n                await _finish_despite_cancellation(\n                    self._finalize_ingestion_run(\n                        run_id=run_id or job_id,\n                        job_id=job_id,\n                        summary=summary,\n                        status=IngestionRunStatus.CANCELLED,\n                        error_message=\"Flow ingestion was cancelled.\",\n                    )\n                )\n            raise\n        except Exception as exc:  # noqa: BLE001 — telemetry must never abort ingestion\n            self.log(f\"Could not begin ingestion-run tracking: {exc}\")\n            return None, None, None, None\n        else:\n            return run_id, job_id, summary, kb_record_id\n\n    async def _finalize_ingestion_run(\n        self,\n        *,\n        run_id: uuid.UUID,\n        job_id: uuid.UUID | None,\n        summary: IngestionSummary,\n        status: IngestionRunStatus,\n        error_message: str | None,\n    ) -> None:\n        \"\"\"Persist the final summary and transition the parent Job.\"\"\"\n        try:\n            from langflow.api.utils import ingestion_run_service\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion-run finalize skipped ({exc}).\")\n            return\n\n        try:\n            if job_id is not None:\n                # The heartbeat also rewrites job_metadata, so it stops first.\n                await get_job_service().stop_keep_alive(job_id)\n            await ingestion_run_service.finalize_run(\n                run_id,\n                summary=summary,\n                status=status,\n                error_message=error_message,\n            )\n            if job_id is not None:\n                terminal_status = {\n                    IngestionRunStatus.FAILED: JobStatus.FAILED,\n                    IngestionRunStatus.CANCELLED: JobStatus.CANCELLED,\n                }.get(status, JobStatus.COMPLETED)\n                await get_job_service().update_job_status(job_id, terminal_status, finished_timestamp=True)\n        except Exception as exc:  # noqa: BLE001 — telemetry must never re-raise\n            self.log(f\"Could not finalize ingestion-run tracking: {exc}\")\n\n    async def _record_kb_status(\n        self,\n        kb_record_id: uuid.UUID,\n        status_value: str,\n        *,\n        failure_reason: str | None = None,\n    ) -> None:\n        \"\"\"Mirror Path A's KB-row status transitions.\"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n            from langflow.services.database.models.knowledge_base.model import KnowledgeBaseStatus\n        except ImportError:\n            return\n        try:\n            await knowledge_base_service.update_status(\n                kb_record_id,\n                status=KnowledgeBaseStatus(status_value),\n                failure_reason=failure_reason,\n            )\n        except Exception as exc:  # noqa: BLE001\n            self.log(f\"Could not update KB status to {status_value}: {exc}\")\n\n    def _parse_user_metadata_dict(self) -> dict[str, Any]:\n        \"\"\"Decode ``metadata_json`` to a dict, or ``{}`` on any error.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return {}\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return {}\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError):\n            return {}\n        return decoded if isinstance(decoded, dict) else {}\n\n    # =====================================================================\n    #                       RETRIEVAL CODE PATH\n    # =====================================================================\n    @property\n    def _user_uuid(self) -> uuid.UUID | None:\n        \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n        if not self.user_id:\n            return None\n        return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n    async def _get_kb_metadata(self, knowledge_base: str | None = None) -> dict:\n        \"\"\"Load this knowledge base's embedding config from its database row.\n\n        The row is the sole authority. There is no on-disk sidecar to fall back\n        to, which is what lets retrieval work identically on a replica whose\n        filesystem never held the KB directory.\n        \"\"\"\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return {}\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return {}\n        record = await knowledge_base_service.get_by_user_and_name(\n            user_uuid,\n            knowledge_base or self.knowledge_base,\n        )\n        if record is None:\n            return {}\n        return knowledge_base_service.record_to_metadata_dict(record)\n\n    async def _backend_from_record(self) -> tuple[str, dict[str, Any]] | None:\n        \"\"\"Read ``(backend_type, backend_config)`` off the KB's database row.\n\n        Returns ``None`` when there is no row to read — a legacy KB predating the\n        record, or bare lfx with no langflow installed. A lookup that *fails* is a\n        different situation and is allowed to propagate: guessing a backend after a\n        database error is how vectors end up in a store nothing queries.\n        \"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return None\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return None\n        record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n        if record is None:\n            return None\n        return record.backend_type or BackendType.CHROMA.value, record.backend_config or {}\n\n    async def _resolve_backend_config(self) -> tuple[str, dict[str, Any]]:\n        \"\"\"Resolve this KB's backend from its database row.\n\n        Ingestion and retrieval both land here, so they cannot disagree about\n        where this KB's vectors live. A backend that cannot be resolved raises\n        rather than defaulting to local storage: guessing is how a pod ends up\n        writing vectors to a local directory while queries follow the real config\n        to a remote store and silently find nothing.\n        \"\"\"\n        resolved = await self._backend_from_record()\n        if resolved is None:\n            msg = (\n                f\"Cannot determine the vector-store backend for knowledge base \"\n                f\"'{self.knowledge_base}': it has no database record. Refusing to fall \"\n                f\"back to local storage, which would write to a different store than \"\n                f\"queries read from. If this knowledge base predates database-backed \"\n                f\"metadata, adopt it with 'langflow reconcile-kb-from-disk'.\"\n            )\n            raise ValueError(msg)\n        return resolved\n\n    def _resolve_model_selection(self, metadata: dict[str, Any]) -> list[dict[str, Any]]:\n        \"\"\"Resolve the ``get_embeddings``-compatible model selection from metadata.\"\"\"\n        model_selection = metadata.get(\"model_selection\")\n        if model_selection:\n            selection_list = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            return [self._hydrate_model_metadata(entry) for entry in selection_list]\n\n        embedding_model_name = metadata.get(\"embedding_model\")\n        embedding_provider = metadata.get(\"embedding_provider\", \"Unknown\")\n        if not embedding_model_name:\n            msg = (\n                f\"Knowledge base '{self.knowledge_base}' has no embedding model recorded; \"\n                \"re-create it with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n\n        match = self._find_catalog_entry(embedding_model_name)\n        if match is None:\n            msg = (\n                f\"Embedding model '{embedding_model_name}' (provider '{embedding_provider}') \"\n                \"recorded for this knowledge base is no longer available in the model registry. \"\n                \"Please re-create the knowledge base with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n        return [match]\n\n    def _hydrate_model_metadata(self, entry: dict[str, Any]) -> dict[str, Any]:\n        \"\"\"Fill in ``metadata.embedding_class`` / ``param_mapping`` if missing.\"\"\"\n        entry_metadata = entry.get(\"metadata\") or {}\n        has_class = bool(entry_metadata.get(\"embedding_class\"))\n        has_mapping = bool(entry_metadata.get(\"param_mapping\"))\n        if has_class and has_mapping:\n            return entry\n\n        model_name = entry.get(\"name\")\n        if not model_name:\n            return entry\n\n        catalog_entry = self._find_catalog_entry(model_name)\n        if catalog_entry is None:\n            return entry\n\n        catalog_metadata = catalog_entry.get(\"metadata\") or {}\n        merged_metadata = {**catalog_metadata, **entry_metadata}\n        return {**entry, \"metadata\": merged_metadata}\n\n    def _find_catalog_entry(self, model_name: str) -> dict[str, Any] | None:\n        \"\"\"Look up an embedding model by name in the unified-models catalog.\"\"\"\n        options = get_embedding_model_options(user_id=self.user_id)\n        return next((o for o in options if o.get(\"name\") == model_name), None)\n\n    async def retrieve_data(self) -> DataFrame:\n        \"\"\"Retrieve data from the selected knowledge base.\n\n        Annotation narrowed to ``DataFrame`` to keep this output's handle\n        type list at ``[\"Table\"]`` only; widening to a union surfaces\n        ``[\"Table\", \"JSON\"]`` on the API and breaks every starter-project\n        edge whose sourceHandle was stored against a single-type handle\n        (BUG-02). The cross-mode defensive fallback may still return a\n        ``Data`` at runtime — Python ignores return annotations, so\n        nothing breaks.\n        \"\"\"\n        if not _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.build_kb_info()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        if not self.user_id:\n            msg = \"User ID is required for fetching Knowledge Base data.\"\n            raise ValueError(msg)\n\n        # Saved flows can load in standalone lfx, but knowledge retrieval\n        # requires the application's user database and storage services.\n        try:\n            from langflow.services.database.models.user.crud import get_user_by_id\n        except ModuleNotFoundError as exc:\n            if exc.name != \"langflow\":\n                raise\n            msg = \"Knowledge Base retrieval requires OpenXFlow's application services. Run this flow in OpenXFlow.\"\n            raise RuntimeError(msg) from None\n\n        async with session_scope() as db:\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n\n        metadata = await self._get_kb_metadata()\n        if not metadata:\n            msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n            raise ValueError(msg)\n\n        model_selection = self._resolve_model_selection(metadata)\n        chunk_size = metadata.get(\"chunk_size\")\n\n        # Resolve where this KB lives before instantiating embeddings: path\n        # containment is a cheap local check, and a request that will be refused\n        # shouldn't first pay for a credential lookup and a provider client.\n        backend_type, _backend_config = await self._resolve_backend_config()\n\n        # ``api_key=None`` is not \"no credential\": ``get_embeddings`` resolves the\n        # provider's key from the user's variables table and then the environment,\n        # exactly as the server-side ingestion path does. The component input just\n        # takes precedence when supplied.\n        embedding_function = get_embeddings(\n            model=model_selection,\n            user_id=self.user_id,\n            api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n            chunk_size=chunk_size,\n        )\n\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            user_metadata_filter = _parse_metadata_filter(getattr(self, \"metadata_filter\", None))\n            use_scores = bool(self.search_query)\n            exact_filter = backend_type == BackendType.SQLITE.value\n            search_k = self.top_k if exact_filter or not user_metadata_filter else self.top_k * 4\n            search_options = {\"source_filter\": user_metadata_filter} if exact_filter else {}\n            results = await backend.similarity_search(\n                query=self.search_query or \"\",\n                k=search_k,\n                with_scores=use_scores,\n                **search_options,\n            )\n            if user_metadata_filter and not exact_filter:\n                results = [\n                    (doc, score) for doc, score in results if _chunk_matches_filter(doc.metadata, user_metadata_filter)\n                ][: self.top_k]\n\n            embeddings_by_key: dict[tuple[str, str], list[float]] = {}\n            if self.include_embeddings and results:\n                # Join each retrieved chunk to its stored embedding. We key on\n                # ``_id`` when present and fall back to page content otherwise,\n                # so KBs populated by direct file upload — whose chunks carry no\n                # ``_id`` — still resolve. See ``_embedding_match_key``.\n                wanted_keys = {_embedding_match_key(doc.page_content, doc.metadata) for doc, _score in results}\n                async with aclosing(backend.iter_documents(include_embeddings=True)) as batches:\n                    async for batch in batches:\n                        for entry in batch:\n                            if entry.embedding is None:\n                                continue\n                            key = _embedding_match_key(entry.content, entry.metadata)\n                            if key in wanted_keys:\n                                embeddings_by_key[key] = entry.embedding\n                        if wanted_keys <= embeddings_by_key.keys():\n                            break\n\n            data_list: list[Data] = []\n            for doc, score in results:\n                kwargs: dict[str, Any] = {\"content\": doc.page_content}\n                if use_scores:\n                    kwargs[\"_score\"] = backend.normalize_score(score)\n                if self.include_metadata:\n                    kwargs.update(doc.metadata)\n                if self.include_embeddings:\n                    kwargs[\"_embeddings\"] = embeddings_by_key.get(_embedding_match_key(doc.page_content, doc.metadata))\n                data_list.append(Data(**kwargs))\n\n            return DataFrame(data=data_list)\n        finally:\n            await backend.teardown()\n\n\ndef _embedding_match_key(content: str, metadata: dict[str, Any] | None) -> tuple[str, str]:\n    \"\"\"Build a stable key for aligning a retrieved chunk with its stored embedding.\n\n    Embeddings are gathered separately (via ``iter_documents``) and then joined\n    back onto the search results. Component-driven ingestion stamps a\n    content-hash ``_id`` on every chunk, but direct file-upload ingestion\n    (``KBIngestionHelper.perform_ingestion``) does not. Keying the join purely on\n    ``_id`` therefore left every upload-populated KB with ``_embeddings: None``.\n\n    We prefer ``_id`` when present (so legitimately distinct chunks that happen to\n    share text aren't collapsed) and fall back to the chunk's page content\n    otherwise. The content fallback is exact for the embedding use case: two\n    chunks with identical text necessarily share the same embedding vector. The\n    leading ``\"id\"`` / ``\"content\"`` tag namespaces the two key spaces so a mixed\n    KB (some chunks with ``_id``, some without) never cross-matches.\n    \"\"\"\n    doc_id = metadata.get(\"_id\") if metadata else None\n    if doc_id:\n        return (\"id\", str(doc_id))\n    return (\"content\", content or \"\")\n\n\ndef _parse_metadata_filter(raw: str | None) -> dict[str, list[str]]:\n    \"\"\"Decode the ``metadata_filter`` input into a {key: [values]} map.\n\n    Empty or malformed input maps to an empty filter so retrieval falls back\n    to the unfiltered path. JSON errors are swallowed here rather than raised:\n    surfacing component-config errors at the canvas node would break a flow\n    run for what is meant to be an optional refinement.\n    \"\"\"\n    if not raw:\n        return {}\n    text = raw.strip() if isinstance(raw, str) else raw\n    if not text:\n        return {}\n    try:\n        decoded = json.loads(text)\n    except (TypeError, json.JSONDecodeError):\n        logger.warning(\"KnowledgeComponent: metadata_filter is not valid JSON; ignoring filter.\")\n        return {}\n    if not isinstance(decoded, dict):\n        logger.warning(\"KnowledgeComponent: metadata_filter must be a JSON object; ignoring filter.\")\n        return {}\n    result: dict[str, list[str]] = {}\n    for key, value in decoded.items():\n        if not isinstance(key, str):\n            continue\n        if isinstance(value, list):\n            result[key] = [str(entry) for entry in value]\n        else:\n            result[key] = [str(value)]\n    return result\n\n\ndef _chunk_matches_filter(metadata: dict[str, Any] | None, filt: dict[str, list[str]]) -> bool:\n    \"\"\"AND across keys, OR within key values, mirroring the chunks endpoint.\"\"\"\n    if not filt:\n        return True\n    if not metadata:\n        return False\n    raw = metadata.get(\"source_metadata\")\n    if not raw:\n        return False\n    try:\n        stored = json.loads(raw) if isinstance(raw, str) else raw\n    except json.JSONDecodeError:\n        return False\n    if not isinstance(stored, dict):\n        return False\n    for key, expected_values in filt.items():\n        actual = stored.get(key)\n        if actual is None:\n            return False\n        actual_set = {str(entry) for entry in actual} if isinstance(actual, list) else {str(actual)}\n        if not actual_set & set(expected_values):\n            return False\n    return True\n"
+                "value": "\"\"\"Unified Knowledge component — ingest into or retrieve from a knowledge base.\n\nThis component merges what used to live in ``ingestion.py`` and\n``retrieval.py`` into a single, mode-driven component. A ``TabInput``\n(\"📥 Ingest\" / \"🔍 Retrieve\") drives which inputs and which output are\nvisible. The merged shape gives users one node per KB instead of two\nparallel ones that always shared the same ``knowledge_base`` picker,\nembedding-model metadata, and backend registry.\n\nBoth legacy classes (``KnowledgeIngestionComponent``,\n``KnowledgeBaseComponent``) remain importable as thin subclasses of\n``KnowledgeComponent`` — see the sibling ``ingestion.py`` / ``retrieval.py``\nmodules — so saved flows continue to load unchanged.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport uuid\nfrom collections.abc import Awaitable, Mapping\nfrom contextlib import aclosing\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, TypeVar\n\nimport anyio\nimport pandas as pd\n\nfrom lfx.base.knowledge_bases.backends import BackendType, BaseVectorStoreBackend\nfrom lfx.base.knowledge_bases.backends.base import BackendConfigurationError, IngestedDocument\nfrom lfx.base.knowledge_bases.backends.naming import ensure_storage_routing_allowed\nfrom lfx.base.knowledge_bases.ingestion_sources.base import (\n    IngestionItemResult,\n    IngestionItemStatus,\n    IngestionRunStatus,\n    IngestionSummary,\n)\nfrom lfx.base.knowledge_bases.ingestion_sources.flow_component import FlowComponentSource\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.knowledge_bases.validation import (\n    is_valid_collection_name as is_valid_kb_collection_name,\n)\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.helpers.base_model import coalesce_bool\nfrom lfx.io import (\n    BoolInput,\n    DBProviderInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MessageTextInput,\n    ModelInput,\n    Output,\n    SecretStrInput,\n    StrInput,\n    TabInput,\n    TableInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n    session_scope,\n)\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n\ndef _inputs_for_mode(default_mode: str) -> list:\n    \"\"\"Return a fresh copy of the canonical inputs list with show flags set for the given mode.\n\n    Used by the legacy subclasses so a saved flow keyed on\n    ``KnowledgeIngestion`` / ``KnowledgeBase`` lands on a node template\n    whose default visibility already matches the pinned mode — no\n    \"wait for the user to click the mode tab\" UX gap on load.\n    \"\"\"\n    always_visible = set(KnowledgeComponent.default_keys) | set(KnowledgeComponent.mode_config[default_mode])\n    inputs_copy = []\n    for inp in KnowledgeComponent.inputs:\n        clone = inp.model_copy(deep=True) if hasattr(inp, \"model_copy\") else inp\n        if clone.name == \"mode\":\n            clone.value = default_mode\n        else:\n            clone.show = clone.name in always_visible\n        inputs_copy.append(clone)\n    return inputs_copy\n\n\nif TYPE_CHECKING:\n    from pathlib import Path\n\n# Mode constants. Plain-text labels (no emoji) for consistency with the rest of\n# Langflow's TabInput palette — see ``MemoryComponent`` for the same convention.\nMODE_INGEST = \"Ingest\"\nMODE_RETRIEVE = \"Retrieve\"\n\n# How often a flow ingestion checks whether its job was cancelled from the\n# Knowledge page while it waits on the embedding provider.\nJOB_CANCEL_POLL_SECONDS = 1.0\n\n\nclass KnowledgeIngestionCancelledError(Exception):\n    \"\"\"The ingestion's job was cancelled from the Knowledge page.\"\"\"\n\n\n_T = TypeVar(\"_T\")\n\n\nasync def _finish_despite_cancellation(awaitable: Awaitable[_T]) -> _T:\n    \"\"\"Run ``awaitable`` to completion even if the caller is cancelled meanwhile.\n\n    ``anyio.CancelScope(shield=True)`` holds off AnyIO cancellation, but a\n    repeated native ``Task.cancel()`` still interrupts it, and a stopped flow\n    build can cancel its task more than once. The work runs in its own task,\n    and the caller waits through every cancellation, re-raising once the work\n    has landed.\n    \"\"\"\n    task = asyncio.ensure_future(awaitable)\n    cancelled = False\n    with anyio.CancelScope(shield=True):\n        while not task.done():\n            try:\n                await asyncio.wait({task})\n            except asyncio.CancelledError:\n                cancelled = True\n    if cancelled:\n        error = None if task.cancelled() else task.exception()\n        if error is not None:\n            logger.warning(f\"Work finished during cancellation failed: {error!r}\")\n        raise asyncio.CancelledError from error\n    return task.result()\n\n\ndef _is_retrieve_mode(value: Any) -> bool:\n    \"\"\"Lenient mode check: treats any label containing 'Retrieve' as retrieve mode.\n\n    Older saved flows may carry the emoji-prefixed labels (\"📥 Ingest\" /\n    \"🔍 Retrieve\") this component used to ship with; substring matching\n    keeps those loading without forcing a flow rewrite.\n    \"\"\"\n    return isinstance(value, str) and \"Retrieve\" in value\n\n\n# Boolean flags of a ``column_config`` row. Toggled cells hold real booleans,\n# but a typed cell (or a flow saved while the cell rendered as plain text)\n# keeps the raw string, e.g. ``\"true\"`` — read them with ``coalesce_bool``.\n_COLUMN_FLAGS = (\"vectorize\", \"identifier\")\n\n\ndef _normalize_column_config(config_list: list[dict[str, Any]]) -> list[dict[str, Any]]:\n    \"\"\"Return copies of the rows with every flag as a real boolean.\"\"\"\n    return [{**row, **{key: coalesce_bool(row.get(key)) for key in _COLUMN_FLAGS}} for row in config_list]\n\n\n# Error message used by both the ingest and retrieve paths when the user is\n# running against an Astra cloud environment that disables these flows.\nastra_error_msg = \"Knowledge ingestion and retrieval are not supported in Astra cloud environment.\"\n\n_DEFAULT_OPENSEARCH_CONFIG = {\n    \"url_variable\": \"OPENSEARCH_URL\",\n    \"username_variable\": \"OPENSEARCH_USERNAME\",\n    \"password_variable\": \"OPENSEARCH_PASSWORD\",  # pragma: allowlist secret\n    \"index_name\": \"\",\n    \"vector_field\": \"vector_field\",\n    \"text_field\": \"text\",\n}\n\n\nclass KnowledgeComponent(Component):\n    \"\"\"One component for both writing into and reading from an OpenXFlow knowledge base.\n\n    A ``TabInput`` switches between ingestion and retrieval. The\n    ``update_build_config`` / ``update_outputs`` hooks hide the inputs\n    and the output that don't apply to the current mode so the canvas\n    node stays focused.\n    \"\"\"\n\n    display_name = \"Knowledge\"\n    description = \"Ingest into or retrieve from an OpenXFlow knowledge base.\"\n    icon = \"database\"\n    name = \"Knowledge\"\n\n    # ------ Mode → visible-fields wiring ---------------------------------\n    # ``default_keys`` are inputs always visible regardless of mode.\n    # ``mode_config`` lists the inputs unique to each mode; everything outside\n    # this set is hidden when its mode is not selected.\n    default_keys: list[str] = [\"mode\", \"knowledge_base\"]\n    mode_config: dict[str, list[str]] = {\n        MODE_INGEST: [\n            \"input_df\",\n            \"column_config\",\n            \"chunk_size\",\n            \"api_key\",\n            \"allow_duplicates\",\n            \"metadata_json\",\n        ],\n        MODE_RETRIEVE: [\n            \"search_query\",\n            \"top_k\",\n            \"include_metadata\",\n            \"include_embeddings\",\n            \"metadata_filter\",\n        ],\n    }\n\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(*args, **kwargs)\n        self._cached_kb_path: Path | None = None\n        self._knowledge_output_result: Data | DataFrame | None = None\n\n    def _pre_run_setup(self) -> None:\n        self._knowledge_output_result = None\n\n    async def _get_output_result(self, output: Output):\n        # Both legacy handles dispatch to the selected mode. Terminal nodes\n        # evaluate both handles, but must execute the storage operation once.\n        if output.method not in {\"build_kb_info\", \"retrieve_data\"}:\n            return await super()._get_output_result(output)\n        if self._knowledge_output_result is None:\n            raw_output = output.model_copy(update={\"options\": None})\n            self._knowledge_output_result = await super()._get_output_result(raw_output)\n        output.value = output.apply_options(self._knowledge_output_result)\n        return output.value\n\n    @staticmethod\n    def _embedding_provider_from_selection(selection: Any) -> str | None:\n        \"\"\"Read a provider name from a raw or persisted model selection.\"\"\"\n        if isinstance(selection, list):\n            selection = selection[0] if selection else None\n        if not isinstance(selection, Mapping):\n            return None\n        provider = selection.get(\"provider\")\n        if not isinstance(provider, str) or not provider.strip():\n            return None\n        return provider.strip()\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Resolve the embedding provider before dialog hooks or runtime secrets.\n\n        New-KB configuration carries its ModelInput inside a dropdown dialog,\n        while existing KBs persist the provider in the KB row. Neither is a\n        top-level ModelInput, so both must be resolved explicitly before the\n        generic component pipeline hydrates ``api_key``.\n        \"\"\"\n        from lfx.base.models.provider_registry import resolve_provider_id\n        from lfx.services.model_provider_policy import ModelProviderPolicyError\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        knowledge_value = effective_parameters.get(\"knowledge_base\", getattr(self, \"knowledge_base\", None))\n\n        if isinstance(knowledge_value, Mapping):\n            if \"02_embedding_model\" not in knowledge_value:\n                return ()\n            provider = self._embedding_provider_from_selection(knowledge_value.get(\"02_embedding_model\"))\n        elif isinstance(knowledge_value, str) and knowledge_value.strip():\n            metadata = await self._get_kb_metadata(knowledge_value.strip())\n            provider = self._embedding_provider_from_selection(metadata.get(\"model_selection\"))\n            if provider is None:\n                legacy_provider = metadata.get(\"embedding_provider\")\n                provider = legacy_provider.strip() if isinstance(legacy_provider, str) else None\n                if provider == \"Unknown\":\n                    provider = None\n        else:\n            return ()\n\n        if provider is None:\n            unknown_provider = \"unknown\"\n            raise ModelProviderPolicyError(unknown_provider, purpose)\n        return (resolve_provider_id(provider),)\n\n    @dataclass\n    class NewKnowledgeBaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"name\": \"create_knowledge_base\",\n                        \"description\": \"Create new knowledge in OpenXFlow.\",\n                        \"display_name\": \"Create new Knowledge Base\",\n                        \"field_order\": [\n                            \"01_new_kb_name\",\n                            \"02_embedding_model\",\n                            \"03_knowledge_backend\",\n                        ],\n                        \"template\": {\n                            \"01_new_kb_name\": StrInput(\n                                name=\"new_kb_name\",\n                                display_name=\"Knowledge Name\",\n                                info=\"Name of the new knowledge to create.\",\n                                required=True,\n                            ),\n                            \"02_embedding_model\": ModelInput(\n                                name=\"embedding_model\",\n                                display_name=\"Choose Embedding Model\",\n                                info=(\n                                    \"Select the embedding model to use for this knowledge base. \"\n                                    \"OpenXFlow uses the configured credentials for that model provider.\"\n                                ),\n                                required=True,\n                                model_type=\"embedding\",\n                            ),\n                            \"03_knowledge_backend\": DBProviderInput(\n                                name=\"knowledge_backend\",\n                                display_name=\"DB Provider\",\n                                info=(\n                                    \"Select where this knowledge base stores vectors. \"\n                                    \"OpenSearch uses the global DB Providers settings.\"\n                                ),\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    # ------ Inputs --------------------------------------------------------\n    # ``knowledge_base`` is kept at position 0 so legacy tests that check\n    # ``component.inputs[0].dialog_inputs`` continue to work.\n    inputs = [\n        DropdownInput(\n            name=\"knowledge_base\",\n            display_name=\"Knowledge\",\n            info=\"Select the knowledge to load data from.\",\n            required=True,\n            options=[],\n            refresh_button=True,\n            real_time_refresh=True,\n            dialog_inputs=asdict(NewKnowledgeBaseInput()),\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[MODE_INGEST, MODE_RETRIEVE],\n            value=MODE_INGEST,\n            info=\"Switch between writing new data into the knowledge base and querying it.\",\n            real_time_refresh=True,\n            tool_mode=True,\n        ),\n        # --- Ingest-only inputs (default-shown; hidden when mode == Retrieve) -\n        HandleInput(\n            name=\"input_df\",\n            display_name=\"Input\",\n            info=(\n                \"Table with all original columns (already chunked / processed). \"\n                \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n                \"it is converted to a DataFrame automatically.\"\n            ),\n            input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n            required=True,\n            dynamic=True,\n            show=True,\n        ),\n        TableInput(\n            name=\"column_config\",\n            display_name=\"Column Configuration\",\n            info=\"Configure column behavior for the knowledge base.\",\n            required=True,\n            table_schema=[\n                {\n                    \"name\": \"column_name\",\n                    \"display_name\": \"Column Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Name of the column in the source DataFrame\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"vectorize\",\n                    \"display_name\": \"Vectorize\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Create embeddings for this column\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"identifier\",\n                    \"display_name\": \"Identifier\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Use this column as unique identifier\",\n                    \"default\": False,\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"column_name\": \"text\",\n                    \"vectorize\": True,\n                    \"identifier\": True,\n                },\n            ],\n            dynamic=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"Batch size for processing embeddings\",\n            advanced=True,\n            value=1000,\n            dynamic=True,\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Embedding Provider API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        BoolInput(\n            name=\"allow_duplicates\",\n            display_name=\"Allow Duplicates\",\n            info=\"Allow duplicate rows in the knowledge base\",\n            advanced=True,\n            value=False,\n            dynamic=True,\n            show=True,\n        ),\n        StrInput(\n            name=\"metadata_json\",\n            display_name=\"Metadata\",\n            info=(\n                \"Optional JSON object of user metadata applied to every chunk produced by this \"\n                'run (e.g. {\"tag\": \"invoice\", \"year\": \"2026\"}). Same shape as the upload modal '\n                \"Metadata section so chunks browser filters + Knowledge retrieval metadata_filter \"\n                \"work uniformly across upload, folder, and flow-driven ingestion. Malformed JSON is \"\n                \"ignored with a warning rather than failing the run.\"\n            ),\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=True,\n        ),\n        # --- Retrieve-only inputs (default-hidden; shown when mode == Retrieve) -\n        MessageTextInput(\n            name=\"search_query\",\n            display_name=\"Search Query\",\n            info=\"Optional search query to filter knowledge base data.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K Results\",\n            info=\"Number of top results to return from the knowledge base.\",\n            value=5,\n            advanced=True,\n            required=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_metadata\",\n            display_name=\"Include Metadata\",\n            info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n            value=True,\n            advanced=False,\n            dynamic=True,\n            show=False,\n        ),\n        BoolInput(\n            name=\"include_embeddings\",\n            display_name=\"Include Embeddings\",\n            info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n            value=False,\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"metadata_filter\",\n            display_name=\"Metadata Filter\",\n            info=(\n                \"Optional JSON object of user-metadata key/value pairs. Only chunks \"\n                'whose source_metadata matches every key are returned (e.g. {\"tag\": \"invoice\"} '\n                'or {\"tag\": [\"invoice\", \"audit\"]} for OR-of-values). Backends without '\n                \"native filtering apply the match client-side after retrieval.\"\n            ),\n            advanced=True,\n            dynamic=True,\n            show=False,\n        ),\n    ]\n\n    # ------ Outputs -------------------------------------------------------\n    # Both outputs are declared at the class level so the runtime can\n    # dispatch to either method depending on which one the saved flow has\n    # wired up. ``update_outputs`` filters the canvas-visible output per\n    # selected mode; see ``TypeConverterComponent`` and ``MemoryComponent``\n    # for the same pattern.\n    #\n    # Output names match the legacy ``KnowledgeIngestionComponent``\n    # (``dataframe_output``) and ``KnowledgeBaseComponent``\n    # (``retrieve_data``) so saved flow edges keyed on those names resolve\n    # cleanly against the merged component.\n    outputs = [\n        Output(\n            display_name=\"Results\",\n            name=\"dataframe_output\",\n            method=\"build_kb_info\",\n            types=[\"JSON\"],\n            selected=\"JSON\",\n        ),\n        Output(\n            display_name=\"Results\",\n            name=\"retrieve_data\",\n            method=\"retrieve_data\",\n            info=\"Returns the data from the selected knowledge base.\",\n            types=[\"Table\"],\n            selected=\"Table\",\n        ),\n    ]\n\n    # ------ Mode-driven UI updates ---------------------------------------\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"Sync the visible output with the current ``mode`` value on canvas load.\n\n        Saved flows hit this path: ``update_outputs`` is normally only triggered\n        by ``real_time_refresh`` field edits, so without this re-sync the\n        canvas could land with both outputs visible.\n        \"\"\"\n        await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = new_frontend_node.get(\"template\", {})\n        mode_value = template.get(\"mode\", {}).get(\"value\", MODE_INGEST)\n        self.update_outputs(new_frontend_node, \"mode\", mode_value)\n        # The rebuilt template carries class-default (Ingest) ``show`` flags,\n        # so a saved Retrieve node returned ``search_query`` hidden and the canvas\n        # silently dropped the edge feeding it.\n        self._apply_mode_visibility(template)\n        return new_frontend_node\n\n    def _apply_mode_visibility(self, build_config, field_name: str | None = None, field_value: Any = None):\n        \"\"\"Show only the inputs that belong to the effective mode.\"\"\"\n        current_mode = build_config.get(\"mode\", {}).get(\"value\") if isinstance(build_config, dict) else None\n        if field_name == \"mode\":\n            current_mode = field_value\n        # Map legacy/emoji-prefixed labels onto the current canonical values so\n        # flows saved before the label change still toggle visibility correctly.\n        if _is_retrieve_mode(current_mode):\n            current_mode = MODE_RETRIEVE\n        elif current_mode not in self.mode_config:\n            current_mode = MODE_INGEST\n        return set_current_fields(\n            build_config=build_config if isinstance(build_config, dotdict) else dotdict(build_config),\n            action_fields=self.mode_config,\n            selected_action=current_mode,\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Filter visible outputs to match the selected mode.\n\n        Triggered by the ``mode`` ``TabInput`` ``real_time_refresh`` flag.\n        The class-level ``outputs`` list always carries both entries so the\n        runtime can resolve either ``build_kb_info`` or ``retrieve_data``\n        regardless of canvas visibility — we just hide the unused one here.\n        \"\"\"\n        if field_name != \"mode\":\n            return frontend_node\n        if _is_retrieve_mode(field_value):\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"retrieve_data\",\n                    method=\"retrieve_data\",\n                    info=\"Returns the data from the selected knowledge base.\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                )\n            ]\n        else:\n            frontend_node[\"outputs\"] = [\n                Output(\n                    display_name=\"Results\",\n                    name=\"dataframe_output\",\n                    method=\"build_kb_info\",\n                    types=[\"JSON\"],\n                    selected=\"JSON\",\n                )\n            ]\n        return frontend_node\n\n    async def update_build_config(\n        self,\n        build_config,\n        field_value: Any,\n        field_name: str | None = None,\n    ):\n        \"\"\"Refresh KB options, drive the create-KB dialog, and hide off-mode fields.\"\"\"\n        # Astra-cloud gate covers both ingest and retrieve paths.\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        # Always populate the create-KB dialog's embedding-model options so the\n        # ModelInput renders correctly regardless of which input triggered the\n        # refresh.\n        try:\n            dialog_template = (\n                build_config[\"knowledge_base\"]\n                .get(\"dialog_inputs\", {})\n                .get(\"fields\", {})\n                .get(\"data\", {})\n                .get(\"node\", {})\n                .get(\"template\", {})\n            )\n            if \"02_embedding_model\" in dialog_template:\n                embedding_options = get_embedding_model_options(user_id=self.user_id)\n                dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n        except Exception:  # noqa: BLE001\n            self.log(\"Failed to populate embedding model options in dialog\")\n\n        # KB-picker refresh + create-new-KB flow (lifted from the legacy ingestion\n        # component verbatim; relied on by both the canvas refresh button and the\n        # dialog-submit path).\n        if field_name == \"knowledge_base\":\n            # Lazy import keeps lfx importable without langflow installed.\n            from langflow.services.database.models.user.crud import get_user_by_id\n\n            async with session_scope() as db:\n                if not self.user_id:\n                    msg = \"User ID is required for fetching knowledge base list.\"\n                    raise ValueError(msg)\n                current_user = await get_user_by_id(db, self.user_id)\n                if not current_user:\n                    msg = f\"User with ID {self.user_id} not found.\"\n                    raise ValueError(msg)\n            if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n                model_selection = field_value[\"02_embedding_model\"]\n                if isinstance(model_selection, dict):\n                    model_selection = [model_selection]\n\n                backend_type, backend_config = self._normalize_backend_selection(\n                    field_value.get(\"03_knowledge_backend\")\n                )\n                ensure_storage_routing_allowed(backend_config, is_superuser=bool(current_user.is_superuser))\n                new_kb_name = field_value[\"01_new_kb_name\"]\n                from langflow.api.utils.kb_helpers import local_chroma_rejection_reason, validate_kb_name\n\n                validate_kb_name(new_kb_name)\n                rejection = local_chroma_rejection_reason(backend_type, backend_config)\n                if rejection:\n                    raise ValueError(rejection)\n\n                embed_model = get_embeddings(\n                    model=model_selection,\n                    user_id=self.user_id,\n                )\n\n                try:\n                    await asyncio.wait_for(\n                        asyncio.to_thread(embed_model.embed_query, \"test\"),\n                        timeout=10,\n                    )\n                except TimeoutError as e:\n                    msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n                    raise ValueError(msg) from e\n                except Exception as e:\n                    msg = f\"Embedding validation failed: {e!s}\"\n                    raise ValueError(msg) from e\n\n                build_config[\"knowledge_base\"][\"value\"] = new_kb_name\n                await self._create_knowledge_base_record(\n                    user_id=self.user_id,\n                    name=field_value[\"01_new_kb_name\"],\n                    model_selection=model_selection,\n                    backend_type=backend_type,\n                    backend_config=backend_config,\n                )\n\n            build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(user_id=self.user_id)\n            if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n                build_config[\"knowledge_base\"][\"value\"] = None\n\n        # Honor the current mode regardless of which field triggered the refresh.\n        return self._apply_mode_visibility(build_config, field_name, field_value)\n\n    # =====================================================================\n    #                       INGESTION CODE PATH\n    # =====================================================================\n    @staticmethod\n    def _scalar_notna(value) -> bool:\n        \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n        ``pd.notna`` returns an array when given an array-like input, which\n        cannot be used directly in a boolean context.  This helper collapses\n        the result to a single scalar ``bool``.\n        \"\"\"\n        result = pd.notna(value)\n        if hasattr(result, \"__iter__\") and not isinstance(result, str):\n            import numpy as np\n\n            arr = np.asarray(result)\n            return arr.size > 0 and arr.all()\n        return bool(result)\n\n    def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n        \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n        if not self.column_config:\n            msg = \"Column configuration cannot be empty\"\n            raise ValueError(msg)\n\n        config_list = self.column_config if isinstance(self.column_config, list) else []\n\n        df_columns = set(df_source.columns)\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            if col_name not in df_columns:\n                msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n                raise ValueError(msg)\n\n        return config_list\n\n    @staticmethod\n    def _extract_source_types_from_df(df_source: pd.DataFrame) -> set[str]:\n        \"\"\"Pull file extensions out of common path/name columns on the source DataFrame.\n\n        The direct-upload ingestion path stores extensions in\n        the KB row's ``source_types`` so the KB list can render the\n        correct file-type icon. When ingestion happens via a connected\n        ``input_df`` (e.g. File → Knowledge) the same field stayed empty and\n        the icon defaulted to a blank tile. We look at the well-known columns\n        the File / S3 / cloud-storage components produce and collect any\n        plausible extension so the icon is consistent across both flows.\n        \"\"\"\n        candidate_columns = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for col in candidate_columns:\n            if col not in df_source.columns:\n                continue\n            for value in df_source[col].dropna():\n                ext = KnowledgeComponent._extension_from_value(value)\n                # Drop anything that doesn't look like an extension (URL\n                # query strings, version segments, etc.) — the icon palette\n                # keys off short alphanumeric tokens like \"pdf\"/\"docx\".\n                if ext:\n                    extensions.add(ext)\n        return extensions\n\n    @staticmethod\n    def _extension_from_value(value: Any) -> str | None:\n        \"\"\"Return a normalized file-extension token from a path / filename / MIME string.\n\n        Accepts values like ``\"report.PDF\"``, ``\"/docs/notes.txt\"``, or\n        ``\"application/pdf\"`` and returns ``\"pdf\"`` / ``\"txt\"``. Returns\n        ``None`` if no plausible extension can be derived.\n        \"\"\"\n        extension_length_limit = 10\n        if value is None:\n            return None\n        text = str(value).strip()\n        if not text:\n            return None\n        # MIME types like ``application/pdf`` carry the canonical extension\n        # in the subtype slot — preserve them so File / S3 messages keyed\n        # only on ``mimetype`` still resolve to an icon.\n        if \"/\" in text and \".\" not in text.rsplit(\"/\", 1)[-1]:\n            subtype = text.rsplit(\"/\", 1)[-1].strip().lower()\n            return subtype if subtype and len(subtype) <= extension_length_limit and subtype.isalnum() else None\n        if \".\" not in text:\n            return None\n        ext = text.rsplit(\".\", 1)[-1].strip().lower()\n        if ext and len(ext) <= extension_length_limit and ext.isalnum():\n            return ext\n        return None\n\n    @classmethod\n    def _extract_source_types_from_mapping(cls, mapping: Any) -> set[str]:\n        \"\"\"Pull extensions from a Message/Data-style ``data`` dict or a plain dict.\"\"\"\n        if not isinstance(mapping, dict):\n            return set()\n        candidate_keys = (\"file_path\", \"file_name\", \"filename\", \"source\", \"path\", \"mimetype\")\n        extensions: set[str] = set()\n        for key in candidate_keys:\n            ext = cls._extension_from_value(mapping.get(key))\n            if ext:\n                extensions.add(ext)\n        return extensions\n\n    @classmethod\n    def _extract_source_types_from_input(cls, input_value: Any) -> set[str]:\n        \"\"\"Pull file extensions out of the raw component input.\n\n        ``convert_to_dataframe`` strips Message/Data fields down to ``text``\n        when projecting onto a DataFrame, so file metadata attached to a\n        File-component \"Raw Content\" output never reaches\n        ``_extract_source_types_from_df``. Looking at the raw input first\n        keeps the KB icon consistent with direct upload.\n        \"\"\"\n        if input_value is None:\n            return set()\n        if isinstance(input_value, list):\n            extensions: set[str] = set()\n            for item in input_value:\n                extensions |= cls._extract_source_types_from_input(item)\n            return extensions\n        if isinstance(input_value, pd.DataFrame):\n            return cls._extract_source_types_from_df(input_value)\n        # Message / Data / JSON all expose a ``data`` dict via the lfx schema.\n        mapping = getattr(input_value, \"data\", None)\n        if mapping is None and isinstance(input_value, dict):\n            mapping = input_value\n        return cls._extract_source_types_from_mapping(mapping)\n\n    async def _refresh_kb_stats(\n        self,\n        *,\n        kb_record_id: Any,\n        backend: BaseVectorStoreBackend,\n        extensions: set[str],\n    ) -> None:\n        \"\"\"Recount the KB from its vector store and write the totals onto the row.\n\n        Backend-agnostic: counts come from ``count`` / ``iter_documents`` /\n        ``storage_size_bytes``, so a Chroma, OpenSearch, or pgVector KB all report\n        real numbers. ``extensions`` are merged into ``source_types`` so the KB\n        list renders the right file-type icon regardless of which ingestion route\n        produced the chunks.\n\n        Best-effort: the chunks are already written and committed by the time this\n        runs, so a stats refresh failure must not fail the ingestion.\n        \"\"\"\n        if kb_record_id is None:\n            return\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return\n\n        try:\n            chunks = await backend.count()\n            characters = 0\n            words = 0\n            async with aclosing(backend.iter_documents()) as batches:\n                async for batch in batches:\n                    for document in batch:\n                        characters += len(document.content)\n                        words += len(document.content.split())\n\n            record = await knowledge_base_service.get_by_id(kb_record_id)\n            existing = set(record.source_types or []) if record is not None else set()\n\n            await knowledge_base_service.update_stats(\n                kb_record_id,\n                chunks=chunks,\n                words=words,\n                characters=characters,\n                size_bytes=await backend.storage_size_bytes(),\n                source_types=sorted(existing | extensions),\n            )\n        except Exception as e:  # noqa: BLE001 — stats are cosmetic; chunks are already written\n            self.log(f\"Warning: Could not refresh knowledge base stats: {e}\")\n\n    @staticmethod\n    def _default_backend_selection() -> tuple[str, dict[str, Any]]:\n        \"\"\"Deployment default for a new KB with no explicit backend chosen.\n\n        Environment-driven: pgVector when ``PGVECTOR_CONNECTION_STRING`` is set,\n        else OpenSearch when ``OPENSEARCH_URL`` is set, else SQLite. An\n        unspecified selection (headless flows, multi-replica) therefore lands on\n        the deployment's shared store.\n        \"\"\"\n        from lfx.base.knowledge_bases.backends.postgres import resolve_default_kb_backend\n\n        backend_type = resolve_default_kb_backend()\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, dict(_DEFAULT_OPENSEARCH_CONFIG)\n        return backend_type, {}\n\n    @classmethod\n    def _normalize_backend_selection(cls, value: Any) -> tuple[str, dict[str, Any]]:\n        \"\"\"Normalize a DBProviderInput value into backend type/config.\"\"\"\n        if not value:\n            return cls._default_backend_selection()\n\n        backend_config: dict[str, Any]\n        if isinstance(value, str):\n            backend_type, backend_config = value, {}\n        elif isinstance(value, dict):\n            backend_type = str(value.get(\"backend_type\") or value.get(\"id\") or BackendType.SQLITE.value)\n            backend_config = value.get(\"backend_config\") or value.get(\"config\") or {}\n        else:\n            msg = \"Invalid vector-store backend selection.\"\n            raise TypeError(msg)\n        if not isinstance(backend_config, dict):\n            msg = \"Vector-store backend configuration must be an object.\"\n            raise TypeError(msg)\n        if backend_type == BackendType.OPENSEARCH.value:\n            return backend_type, {**_DEFAULT_OPENSEARCH_CONFIG, **backend_config}\n        if backend_type in {BackendType.SQLITE.value, BackendType.POSTGRES.value}:\n            return backend_type, backend_config\n        msg = \"Chroma is retired. Choose SQLite, pgVector, or OpenSearch for a new knowledge base.\"\n        raise ValueError(msg)\n\n    async def _create_knowledge_base_record(\n        self,\n        *,\n        user_id: Any,\n        name: str,\n        model_selection: list[dict[str, Any]],\n        backend_type: str,\n        backend_config: dict[str, Any],\n    ) -> None:\n        \"\"\"Persist the component-created KB in the DB when Langflow is available.\"\"\"\n        from langflow.api.utils import knowledge_base_service\n\n        await knowledge_base_service.create_record(\n            user_id=user_id,\n            name=name,\n            model_selection=model_selection,\n            column_config=_normalize_column_config(self.column_config) if isinstance(self.column_config, list) else [],\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n        \"\"\"Build detailed column metadata.\"\"\"\n        metadata: dict[str, Any] = {\n            \"total_columns\": len(df_source.columns),\n            \"mapped_columns\": len(config_list),\n            \"unmapped_columns\": len(df_source.columns) - len(config_list),\n            \"columns\": [],\n            \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n        }\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n            vectorize = coalesce_bool(config.get(\"vectorize\"))\n            identifier = coalesce_bool(config.get(\"identifier\"))\n\n            metadata[\"columns\"].append(\n                {\n                    \"name\": col_name,\n                    \"vectorize\": vectorize,\n                    \"identifier\": identifier,\n                }\n            )\n\n            if vectorize:\n                metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n            if identifier:\n                metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n        return metadata\n\n    async def _create_vector_store(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        \"\"\"Create vector store using the configured DB provider.\"\"\"\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            return await self._ingest_into_backend(backend, df_source, config_list, embedding_function, job_id=job_id)\n        except BaseException:\n            await _finish_despite_cancellation(backend.teardown())\n            raise\n\n    async def _ingest_into_backend(\n        self,\n        backend: BaseVectorStoreBackend,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        embedding_function,\n        *,\n        job_id: uuid.UUID | None = None,\n    ) -> BaseVectorStoreBackend:\n        await backend.ensure_ready()\n\n        from langflow.services.knowledge_base_storage.runtime import operation, resolve_record\n\n        record = await resolve_record(self._user_uuid, self.knowledge_base)\n        async with operation(record, shared=True):\n            existing_ids = set()\n            if not self.allow_duplicates:\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        for document in batch:\n                            doc_id = document.metadata.get(\"_id\")\n                            if doc_id:\n                                existing_ids.add(doc_id)\n\n        data_objects = await self._convert_df_to_data_objects(df_source, config_list, existing_ids=existing_ids)\n\n        user_metadata_tag = self._resolve_user_metadata_tag()\n\n        documents = []\n        for data_obj in data_objects:\n            doc = data_obj.to_lc_document()\n            if user_metadata_tag:\n                doc.metadata[\"source_metadata\"] = user_metadata_tag\n            documents.append(doc)\n        if not documents:\n            return backend\n\n        # No storage lease may surround the provider call. The write lease\n        # below rechecks routing and IDs after concurrent operations finish.\n        vectors = await self._embed_unless_job_cancelled(\n            embedding_function, [doc.page_content for doc in documents], job_id\n        )\n        if len(vectors) != len(documents):\n            msg = \"Embedding provider returned an incorrect number of vectors\"\n            raise BackendConfigurationError(msg)\n        embedded = [\n            IngestedDocument(doc.page_content, doc.metadata, vector, id=doc.id)\n            for doc, vector in zip(documents, vectors, strict=True)\n        ]\n        async with operation(record):\n            # A cancel that landed after the embeddings returned must still\n            # keep this run's chunks out of the KB.\n            await self._raise_if_job_cancelled(job_id)\n            if not self.allow_duplicates:\n                current_ids = set()\n                async with aclosing(backend.iter_documents()) as batches:\n                    async for batch in batches:\n                        current_ids.update(doc.metadata[\"_id\"] for doc in batch if doc.metadata.get(\"_id\"))\n                unique = []\n                for doc in embedded:\n                    doc_id = doc.metadata.get(\"_id\")\n                    if doc_id and doc_id in current_ids:\n                        continue\n                    if doc_id:\n                        current_ids.add(doc_id)\n                    unique.append(doc)\n                embedded = unique\n\n            if embedded:\n                await backend.add_embedded_documents(embedded)\n                self.log(f\"Added {len(embedded)} documents to vector store '{self.knowledge_base}'\")\n\n        return backend\n\n    async def _embed_unless_job_cancelled(\n        self, embedding_function, texts: list[str], job_id: uuid.UUID | None\n    ) -> list[list[float]]:\n        \"\"\"Embed ``texts``, giving up as soon as the run's job is cancelled.\n\n        The Knowledge page cancels a run by marking its job. The embedding call\n        is the long, write-free part of a flow ingestion, so it is the safe\n        place to stop.\n        \"\"\"\n        await self._raise_if_job_cancelled(job_id)\n        if job_id is None:\n            return await embedding_function.aembed_documents(texts)\n\n        # A task group would wrap provider errors in an ExceptionGroup, so a\n        # plain watcher task cancels this scope instead.\n        with anyio.CancelScope() as scope:\n            watcher = asyncio.ensure_future(self._cancel_when_job_cancelled(job_id, scope))\n            try:\n                return await embedding_function.aembed_documents(texts)\n            finally:\n                # The watcher may be mid-poll; let its DB session close.\n                watcher.cancel()\n                with anyio.CancelScope(shield=True):\n                    await asyncio.wait({watcher})\n        raise self._cancelled_error()\n\n    async def _cancel_when_job_cancelled(self, job_id: uuid.UUID, scope: anyio.CancelScope) -> None:\n        # The cancel is a job-row write from another request, so it is polled.\n        while True:\n            await asyncio.sleep(JOB_CANCEL_POLL_SECONDS)\n            if await self._job_cancelled(job_id):\n                scope.cancel()\n                return\n\n    async def _raise_if_job_cancelled(self, job_id: uuid.UUID | None) -> None:\n        if await self._job_cancelled(job_id):\n            raise self._cancelled_error()\n\n    def _cancelled_error(self) -> KnowledgeIngestionCancelledError:\n        return KnowledgeIngestionCancelledError(f\"Ingestion into '{self.knowledge_base}' was cancelled.\")\n\n    async def _job_cancelled(self, job_id: uuid.UUID | None) -> bool:\n        if job_id is None:\n            return False\n        try:\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError:\n            return False\n        try:\n            job = await get_job_service().get_job_by_job_id(job_id)\n        except Exception as exc:  # noqa: BLE001 — a failed poll must not abort the ingestion\n            self.log(f\"Could not check whether ingestion job {job_id} was cancelled: {exc}\")\n            return False\n        return job is not None and job.status == JobStatus.CANCELLED\n\n    async def _convert_df_to_data_objects(\n        self,\n        df_source: pd.DataFrame,\n        config_list: list[dict[str, Any]],\n        existing_ids: set[str] | None = None,\n    ) -> list[Data]:\n        \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n        data_objects: list[Data] = []\n\n        if existing_ids is None:\n            existing_ids = set()\n\n        content_cols = []\n        identifier_cols = []\n\n        for config in config_list:\n            col_name = config.get(\"column_name\")\n\n            if coalesce_bool(config.get(\"vectorize\")):\n                content_cols.append(col_name)\n            if coalesce_bool(config.get(\"identifier\")):\n                identifier_cols.append(col_name)\n\n        for _, row in df_source.iterrows():\n            identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n            page_content = \" \".join(identifier_parts)\n\n            data_dict = {\n                \"text\": page_content,\n            }\n\n            if identifier_cols:\n                identifier_parts = [\n                    str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n                ]\n                page_content = \" \".join(identifier_parts)\n\n            for col in df_source.columns:\n                if col not in content_cols and col in row and self._scalar_notna(row[col]):\n                    value = row[col]\n                    data_dict[col] = str(value)\n\n            page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n            data_dict[\"_id\"] = page_content_hash\n\n            if not self.allow_duplicates and page_content_hash in existing_ids:\n                self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n                continue\n\n            data_obj = Data(data=data_dict)\n            data_objects.append(data_obj)\n\n        return data_objects\n\n    def is_valid_collection_name(self, name: str) -> bool:\n        \"\"\"Return whether ``name`` satisfies the shared collection-name contract.\"\"\"\n        return is_valid_kb_collection_name(name)\n\n    def _resolve_store_path(\n        self,\n        kb_user: str,\n        backend_type: str,\n        backend_config: dict[str, Any] | None,\n    ) -> Path | None:\n        \"\"\"Return this KB's on-disk directory, or ``None`` when it needs no disk.\n\n        Only local Chroma stores anything on this box; every other backend\n        resolves to ``None`` and never touches the filesystem.\n        \"\"\"\n        # Lazy import keeps lfx importable without langflow's DB services.\n        from langflow.api.utils.kb_helpers import resolve_local_store_path\n\n        return resolve_local_store_path(\n            self.knowledge_base,\n            kb_user,\n            backend_type=backend_type,\n            backend_config=backend_config,\n        )\n\n    async def _kb_username(self) -> str:\n        \"\"\"Resolve the owning user's username (used to root a local-Chroma path).\"\"\"\n        cached = getattr(self, \"_cached_kb_username\", None)\n        if cached is not None:\n            return cached\n\n        # Lazy import to keep ``lfx`` importable standalone — langflow's\n        # user/DB models are not always available at module load time.\n        from langflow.services.database.models.user.crud import get_user_by_id\n\n        async with session_scope() as db:\n            if not self.user_id:\n                msg = \"User ID is required for fetching knowledge base path.\"\n                raise ValueError(msg)\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n            self._cached_kb_username = current_user.username\n\n        return self._cached_kb_username\n\n    async def _kb_path(self) -> Path | None:\n        \"\"\"Resolve the KB's local directory from its configured backend.\"\"\"\n        cached_path = getattr(self, \"_cached_kb_path\", None)\n        if cached_path is not None:\n            return cached_path\n\n        backend_type, backend_config = await self._resolve_backend_config()\n        kb_user = await self._kb_username()\n        self._cached_kb_path = self._resolve_store_path(kb_user, backend_type, backend_config)\n        return self._cached_kb_path\n\n    def _resolve_user_metadata_tag(self) -> str:\n        \"\"\"Return the JSON-encoded user metadata tag for chunk writes.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return \"\"\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return \"\"\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError) as exc:\n            self.log(f\"KnowledgeComponent: metadata_json is not valid JSON ({exc}); skipping metadata stamp.\")\n            return \"\"\n        if not isinstance(decoded, dict):\n            self.log(\"KnowledgeComponent: metadata_json must decode to a JSON object; skipping metadata stamp.\")\n            return \"\"\n        return json.dumps(decoded, sort_keys=True)\n\n    async def build_kb_info(self) -> Data:\n        \"\"\"Main ingestion routine → returns a dict with KB metadata.\n\n        The annotation is intentionally narrowed to ``Data`` even though the\n        cross-mode fallback below may return a ``DataFrame`` from\n        ``retrieve_data``. The frontend builds React-Flow handle IDs from\n        this output's type list; widening it to ``Data | DataFrame`` makes\n        the API advertise ``[\"JSON\", \"Table\"]`` for the ingest output and\n        breaks every saved-edge sourceHandle that was generated against\n        a single-type handle (BUG-02). Python doesn't enforce return\n        annotations at runtime, so the rare fallback path keeps working.\n        \"\"\"\n        if _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.retrieve_data()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        run_id: uuid.UUID | None = None\n        run_job_id: uuid.UUID | None = None\n        run_summary: IngestionSummary | None = None\n        run_status: IngestionRunStatus = IngestionRunStatus.SUCCEEDED\n        run_error: str | None = None\n        kb_record_id: uuid.UUID | None = None\n        previous_kb_status = \"ready\"\n        previous_failure_reason: str | None = None\n        try:\n            input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n            df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n            config_list = self._validate_column_config(df_source)\n            column_metadata = self._build_column_metadata(config_list, df_source)\n\n            # Embedding config comes from the ``knowledge_base`` row — the sole\n            # authority. No sidecar is read, so ingestion resolves the same model\n            # on any replica.\n            stored_metadata = await self._get_kb_metadata()\n            # A cancelled run restores this status. Only an earlier failure is\n            # kept; \"empty\" is an API projection of a ready KB with no chunks.\n            if stored_metadata.get(\"status\") == \"failed\":\n                previous_kb_status = \"failed\"\n                previous_failure_reason = stored_metadata.get(\"failure_reason\")\n            model_selection = stored_metadata.get(\"model_selection\")\n            if model_selection:\n                model_selection = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            else:\n                embedding_model_name = stored_metadata.get(\"embedding_model\")\n                embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n                if embedding_model_name:\n                    try:\n                        all_options = get_embedding_model_options(user_id=self.user_id)\n                        match = next(\n                            (o for o in all_options if o.get(\"name\") == embedding_model_name),\n                            None,\n                        )\n                        if match:\n                            model_selection = [match]\n                        else:\n                            self.log(\n                                f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n                                \"from the knowledge base record is no longer available in the model registry. \"\n                                \"Please re-create this knowledge base with a supported embedding model.\"\n                            )\n                            msg = (\n                                f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n                                \"The knowledge base was created with an older format and the model \"\n                                \"is not available in the current registry. \"\n                                \"Please re-create the knowledge base with a supported embedding model.\"\n                            )\n                            raise ValueError(msg)\n                    except ValueError:\n                        raise\n                    except Exception:  # noqa: BLE001\n                        self.log(\n                            f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n                            \"Please re-create this knowledge base with a supported embedding model.\"\n                        )\n                        msg = (\n                            f\"Could not look up embedding model '{embedding_model_name}' \"\n                            f\"(provider: {embedding_provider}). \"\n                            \"Please re-create the knowledge base with a supported embedding model.\"\n                        )\n                        raise ValueError(msg)  # noqa: B904\n\n            if not model_selection:\n                msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n                raise ValueError(msg)\n\n            # ``api_key=None`` falls through to the user's variables table and\n            # then the environment inside ``get_embeddings`` — the same\n            # resolution the server-side ingestion path uses. The component input\n            # only overrides it.\n            embedding_function = get_embeddings(\n                model=model_selection,\n                user_id=self.user_id,\n                api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n                chunk_size=self.chunk_size,\n            )\n\n            kb_path = await self._kb_path()\n\n            run_id, run_job_id, run_summary, kb_record_id = await self._begin_ingestion_run(kb_path)\n            if kb_record_id is not None:\n                await self._record_kb_status(kb_record_id, \"ingesting\")\n\n            backend = await self._create_vector_store(\n                df_source, config_list, embedding_function=embedding_function, job_id=run_job_id\n            )\n\n            try:\n                # Stamp the KB with the file extensions we just ingested so\n                # the Knowledge Bases list renders the correct icon for\n                # flow-driven ingestion (input_df), matching direct upload.\n                # We look at the raw input first because ``convert_to_dataframe``\n                # drops Message/Data metadata fields (file_path, mimetype, …)\n                # when projecting onto the DataFrame.\n                source_types = self._extract_source_types_from_input(input_value)\n                source_types |= self._extract_source_types_from_df(df_source)\n                # Storage routing returns a guarded proxy with the backend's\n                # methods, so a concrete-class check would skip the refresh.\n                await self._refresh_kb_stats(\n                    kb_record_id=kb_record_id,\n                    backend=backend,\n                    extensions=source_types,\n                )\n            finally:\n                await _finish_despite_cancellation(backend.teardown())\n\n            meta: dict[str, Any] = {\n                \"kb_id\": str(uuid.uuid4()),\n                \"kb_name\": self.knowledge_base,\n                \"rows\": len(df_source),\n                \"column_metadata\": column_metadata,\n                \"path\": str(kb_path),\n                \"config_columns\": len(config_list),\n                \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n            }\n\n            if run_summary is not None:\n                run_summary.record_item(\n                    IngestionItemResult(\n                        item_id=self.knowledge_base,\n                        display_name=f\"{self.knowledge_base} ({len(df_source)} rows)\",\n                        status=IngestionItemStatus.SUCCEEDED,\n                        chunks_created=len(df_source),\n                    ),\n                )\n\n            if kb_record_id is not None:\n                # Stats were already refreshed straight from the backend above.\n                await self._record_kb_status(kb_record_id, \"ready\")\n\n            self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n            return Data(data=meta)\n\n        except asyncio.CancelledError:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = \"Flow ingestion was cancelled.\"\n            raise\n        except KnowledgeIngestionCancelledError as e:\n            run_status = IngestionRunStatus.CANCELLED\n            run_error = str(e)\n            raise\n        except (OSError, ValueError, RuntimeError, KeyError) as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            msg = f\"Error during KB ingestion: {e}\"\n            raise RuntimeError(msg) from e\n        except Exception as e:\n            run_status = IngestionRunStatus.FAILED\n            run_error = str(e) or e.__class__.__name__\n            raise\n        finally:\n\n            async def settle_run() -> None:\n                if run_id is not None and run_summary is not None:\n                    await self._finalize_ingestion_run(\n                        run_id=run_id,\n                        job_id=run_job_id,\n                        summary=run_summary,\n                        status=run_status,\n                        error_message=run_error,\n                    )\n                if kb_record_id is not None:\n                    if run_status is IngestionRunStatus.FAILED:\n                        await self._record_kb_status(kb_record_id, \"failed\", failure_reason=run_error)\n                    elif run_status is IngestionRunStatus.CANCELLED:\n                        await self._record_kb_status(\n                            kb_record_id, previous_kb_status, failure_reason=previous_failure_reason\n                        )\n\n            # A stopped build can cancel this task more than once, and the\n            # writes that make the run terminal must survive every cancel.\n            await _finish_despite_cancellation(settle_run())\n\n    async def _begin_ingestion_run(\n        self,\n        kb_path: Path,\n    ) -> tuple[uuid.UUID | None, uuid.UUID | None, IngestionSummary | None, uuid.UUID | None]:\n        \"\"\"Create a parent ``Job`` and seed an ingestion-run row.\"\"\"\n        if not self.user_id:\n            self.log(\"No user_id on component; skipping ingestion-run tracking.\")\n            return None, None, None, None\n\n        try:\n            user_uuid = uuid.UUID(str(self.user_id))\n        except (ValueError, TypeError) as exc:\n            self.log(f\"Could not coerce user_id={self.user_id!r} to UUID; skipping run tracking ({exc}).\")\n            return None, None, None, None\n\n        try:\n            from langflow.api.utils import ingestion_run_service, knowledge_base_service\n            from langflow.services.database.models.jobs.model import JobStatus, JobType\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion will not be recorded ({exc}).\")\n            return None, None, None, None\n\n        job_id: uuid.UUID | None = None\n        run_id: uuid.UUID | None = None\n        summary: IngestionSummary | None = None\n        try:\n            kb_record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n            kb_record_id = kb_record.id if kb_record is not None else None\n\n            user_metadata = self._parse_user_metadata_dict()\n\n            job_id = uuid.uuid4()\n            job_service = get_job_service()\n            raw_flow_id = getattr(self, \"flow_id\", None)\n            flow_id_uuid = uuid.UUID(str(raw_flow_id)) if raw_flow_id else job_id\n            source = FlowComponentSource(\n                user_id=user_uuid,\n                source_config={\n                    \"knowledge_base\": self.knowledge_base,\n                    \"kb_path\": str(kb_path),\n                    \"flow_id\": str(flow_id_uuid),\n                },\n            )\n            summary = IngestionSummary(\n                kb_name=self.knowledge_base,\n                source_type=source.source_type.value,\n                user_id=user_uuid,\n                job_id=job_id,\n                source_config=source.describe().get(\"config\") or {},\n                user_metadata=user_metadata,\n            )\n            # Inserted running and already heartbeated, so an orphan sweep in\n            # another worker never sees this live run without a heartbeat.\n            await job_service.create_job(\n                job_id=job_id,\n                flow_id=flow_id_uuid,\n                job_type=JobType.INGESTION,\n                asset_id=kb_record_id,\n                asset_type=\"knowledge_base\",\n                user_id=user_uuid,\n                status=JobStatus.IN_PROGRESS,\n                heartbeat=True,\n            )\n\n            run_id = await ingestion_run_service.create_run(\n                kb_name=self.knowledge_base,\n                source=source,\n                job_id=job_id,\n                user_id=user_uuid,\n                kb_id=kb_record_id,\n                user_metadata=user_metadata,\n            )\n            if run_id is None:\n                return None, job_id, None, kb_record_id\n            await ingestion_run_service.mark_running(run_id)\n\n            self.log(f\"Started ingestion run job_id={job_id} kb_name={self.knowledge_base} kb_id={kb_record_id}\")\n            # Keeps the job heartbeated until _finalize_ingestion_run stops it.\n            await job_service.start_keep_alive(job_id)\n        except asyncio.CancelledError:\n            # The caller has not received the run ID yet, so it cannot finalize\n            # a cancellation that arrives while tracking is being initialized.\n            if job_id is not None and summary is not None:\n                await _finish_despite_cancellation(\n                    self._finalize_ingestion_run(\n                        run_id=run_id or job_id,\n                        job_id=job_id,\n                        summary=summary,\n                        status=IngestionRunStatus.CANCELLED,\n                        error_message=\"Flow ingestion was cancelled.\",\n                    )\n                )\n            raise\n        except Exception as exc:  # noqa: BLE001 — telemetry must never abort ingestion\n            self.log(f\"Could not begin ingestion-run tracking: {exc}\")\n            return None, None, None, None\n        else:\n            return run_id, job_id, summary, kb_record_id\n\n    async def _finalize_ingestion_run(\n        self,\n        *,\n        run_id: uuid.UUID,\n        job_id: uuid.UUID | None,\n        summary: IngestionSummary,\n        status: IngestionRunStatus,\n        error_message: str | None,\n    ) -> None:\n        \"\"\"Persist the final summary and transition the parent Job.\"\"\"\n        try:\n            from langflow.api.utils import ingestion_run_service\n            from langflow.services.database.models.jobs.model import JobStatus\n            from langflow.services.deps import get_job_service\n        except ImportError as exc:\n            self.log(f\"Run-history wiring unavailable; ingestion-run finalize skipped ({exc}).\")\n            return\n\n        try:\n            if job_id is not None:\n                # The heartbeat also rewrites job_metadata, so it stops first.\n                await get_job_service().stop_keep_alive(job_id)\n            await ingestion_run_service.finalize_run(\n                run_id,\n                summary=summary,\n                status=status,\n                error_message=error_message,\n            )\n            if job_id is not None:\n                terminal_status = {\n                    IngestionRunStatus.FAILED: JobStatus.FAILED,\n                    IngestionRunStatus.CANCELLED: JobStatus.CANCELLED,\n                }.get(status, JobStatus.COMPLETED)\n                await get_job_service().update_job_status(job_id, terminal_status, finished_timestamp=True)\n        except Exception as exc:  # noqa: BLE001 — telemetry must never re-raise\n            self.log(f\"Could not finalize ingestion-run tracking: {exc}\")\n\n    async def _record_kb_status(\n        self,\n        kb_record_id: uuid.UUID,\n        status_value: str,\n        *,\n        failure_reason: str | None = None,\n    ) -> None:\n        \"\"\"Mirror Path A's KB-row status transitions.\"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n            from langflow.services.database.models.knowledge_base.model import KnowledgeBaseStatus\n        except ImportError:\n            return\n        try:\n            await knowledge_base_service.update_status(\n                kb_record_id,\n                status=KnowledgeBaseStatus(status_value),\n                failure_reason=failure_reason,\n            )\n        except Exception as exc:  # noqa: BLE001\n            self.log(f\"Could not update KB status to {status_value}: {exc}\")\n\n    def _parse_user_metadata_dict(self) -> dict[str, Any]:\n        \"\"\"Decode ``metadata_json`` to a dict, or ``{}`` on any error.\"\"\"\n        raw = getattr(self, \"metadata_json\", None)\n        if not raw:\n            return {}\n        text = raw.strip() if isinstance(raw, str) else raw\n        if not text:\n            return {}\n        try:\n            decoded = json.loads(text)\n        except (TypeError, json.JSONDecodeError):\n            return {}\n        return decoded if isinstance(decoded, dict) else {}\n\n    # =====================================================================\n    #                       RETRIEVAL CODE PATH\n    # =====================================================================\n    @property\n    def _user_uuid(self) -> uuid.UUID | None:\n        \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n        if not self.user_id:\n            return None\n        return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n    async def _get_kb_metadata(self, knowledge_base: str | None = None) -> dict:\n        \"\"\"Load this knowledge base's embedding config from its database row.\n\n        The row is the sole authority. There is no on-disk sidecar to fall back\n        to, which is what lets retrieval work identically on a replica whose\n        filesystem never held the KB directory.\n        \"\"\"\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return {}\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return {}\n        record = await knowledge_base_service.get_by_user_and_name(\n            user_uuid,\n            knowledge_base or self.knowledge_base,\n        )\n        if record is None:\n            return {}\n        return knowledge_base_service.record_to_metadata_dict(record)\n\n    async def _backend_from_record(self) -> tuple[str, dict[str, Any]] | None:\n        \"\"\"Read ``(backend_type, backend_config)`` off the KB's database row.\n\n        Returns ``None`` when there is no row to read — a legacy KB predating the\n        record, or bare lfx with no langflow installed. A lookup that *fails* is a\n        different situation and is allowed to propagate: guessing a backend after a\n        database error is how vectors end up in a store nothing queries.\n        \"\"\"\n        try:\n            from langflow.api.utils import knowledge_base_service\n        except ImportError:\n            return None\n\n        user_uuid = self._user_uuid\n        if user_uuid is None:\n            return None\n        record = await knowledge_base_service.get_by_user_and_name(user_uuid, self.knowledge_base)\n        if record is None:\n            return None\n        return record.backend_type or BackendType.CHROMA.value, record.backend_config or {}\n\n    async def _resolve_backend_config(self) -> tuple[str, dict[str, Any]]:\n        \"\"\"Resolve this KB's backend from its database row.\n\n        Ingestion and retrieval both land here, so they cannot disagree about\n        where this KB's vectors live. A backend that cannot be resolved raises\n        rather than defaulting to local storage: guessing is how a pod ends up\n        writing vectors to a local directory while queries follow the real config\n        to a remote store and silently find nothing.\n        \"\"\"\n        resolved = await self._backend_from_record()\n        if resolved is None:\n            msg = (\n                f\"Cannot determine the vector-store backend for knowledge base \"\n                f\"'{self.knowledge_base}': it has no database record. Refusing to fall \"\n                f\"back to local storage, which would write to a different store than \"\n                f\"queries read from. If this knowledge base predates database-backed \"\n                f\"metadata, adopt it with 'langflow reconcile-kb-from-disk'.\"\n            )\n            raise ValueError(msg)\n        return resolved\n\n    def _resolve_model_selection(self, metadata: dict[str, Any]) -> list[dict[str, Any]]:\n        \"\"\"Resolve the ``get_embeddings``-compatible model selection from metadata.\"\"\"\n        model_selection = metadata.get(\"model_selection\")\n        if model_selection:\n            selection_list = [model_selection] if isinstance(model_selection, dict) else list(model_selection)\n            return [self._hydrate_model_metadata(entry) for entry in selection_list]\n\n        embedding_model_name = metadata.get(\"embedding_model\")\n        embedding_provider = metadata.get(\"embedding_provider\", \"Unknown\")\n        if not embedding_model_name:\n            msg = (\n                f\"Knowledge base '{self.knowledge_base}' has no embedding model recorded; \"\n                \"re-create it with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n\n        match = self._find_catalog_entry(embedding_model_name)\n        if match is None:\n            msg = (\n                f\"Embedding model '{embedding_model_name}' (provider '{embedding_provider}') \"\n                \"recorded for this knowledge base is no longer available in the model registry. \"\n                \"Please re-create the knowledge base with a supported embedding model.\"\n            )\n            raise ValueError(msg)\n        return [match]\n\n    def _hydrate_model_metadata(self, entry: dict[str, Any]) -> dict[str, Any]:\n        \"\"\"Fill in ``metadata.embedding_class`` / ``param_mapping`` if missing.\"\"\"\n        entry_metadata = entry.get(\"metadata\") or {}\n        has_class = bool(entry_metadata.get(\"embedding_class\"))\n        has_mapping = bool(entry_metadata.get(\"param_mapping\"))\n        if has_class and has_mapping:\n            return entry\n\n        model_name = entry.get(\"name\")\n        if not model_name:\n            return entry\n\n        catalog_entry = self._find_catalog_entry(model_name)\n        if catalog_entry is None:\n            return entry\n\n        catalog_metadata = catalog_entry.get(\"metadata\") or {}\n        merged_metadata = {**catalog_metadata, **entry_metadata}\n        return {**entry, \"metadata\": merged_metadata}\n\n    def _find_catalog_entry(self, model_name: str) -> dict[str, Any] | None:\n        \"\"\"Look up an embedding model by name in the unified-models catalog.\"\"\"\n        options = get_embedding_model_options(user_id=self.user_id)\n        return next((o for o in options if o.get(\"name\") == model_name), None)\n\n    async def retrieve_data(self) -> DataFrame:\n        \"\"\"Retrieve data from the selected knowledge base.\n\n        Annotation narrowed to ``DataFrame`` to keep this output's handle\n        type list at ``[\"Table\"]`` only; widening to a union surfaces\n        ``[\"Table\", \"JSON\"]`` on the API and breaks every starter-project\n        edge whose sourceHandle was stored against a single-type handle\n        (BUG-02). The cross-mode defensive fallback may still return a\n        ``Data`` at runtime — Python ignores return annotations, so\n        nothing breaks.\n        \"\"\"\n        if not _is_retrieve_mode(getattr(self, \"mode\", MODE_INGEST)):\n            return await self.build_kb_info()\n        raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n        if not self.user_id:\n            msg = \"User ID is required for fetching Knowledge Base data.\"\n            raise ValueError(msg)\n\n        # Saved flows can load in standalone lfx, but knowledge retrieval\n        # requires the application's user database and storage services.\n        try:\n            from langflow.services.database.models.user.crud import get_user_by_id\n        except ModuleNotFoundError as exc:\n            if exc.name != \"langflow\":\n                raise\n            msg = \"Knowledge Base retrieval requires OpenXFlow's application services. Run this flow in OpenXFlow.\"\n            raise RuntimeError(msg) from None\n\n        async with session_scope() as db:\n            current_user = await get_user_by_id(db, self.user_id)\n            if not current_user:\n                msg = f\"User with ID {self.user_id} not found.\"\n                raise ValueError(msg)\n\n        metadata = await self._get_kb_metadata()\n        if not metadata:\n            msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n            raise ValueError(msg)\n\n        model_selection = self._resolve_model_selection(metadata)\n        chunk_size = metadata.get(\"chunk_size\")\n\n        # Resolve where this KB lives before instantiating embeddings: path\n        # containment is a cheap local check, and a request that will be refused\n        # shouldn't first pay for a credential lookup and a provider client.\n        backend_type, _backend_config = await self._resolve_backend_config()\n\n        # ``api_key=None`` is not \"no credential\": ``get_embeddings`` resolves the\n        # provider's key from the user's variables table and then the environment,\n        # exactly as the server-side ingestion path does. The component input just\n        # takes precedence when supplied.\n        embedding_function = get_embeddings(\n            model=model_selection,\n            user_id=self.user_id,\n            api_key=self.api_key if getattr(self, \"api_key\", None) else None,\n            chunk_size=chunk_size,\n        )\n\n        from langflow.api.utils.kb_helpers import backend_for_name\n\n        owner_id = self._user_uuid\n        if owner_id is None:\n            msg = \"User ID is required for knowledge base storage.\"\n            raise ValueError(msg)\n        backend = await backend_for_name(owner_id, self.knowledge_base, embedding_function=embedding_function)\n        try:\n            user_metadata_filter = _parse_metadata_filter(getattr(self, \"metadata_filter\", None))\n            use_scores = bool(self.search_query)\n            exact_filter = backend_type == BackendType.SQLITE.value\n            search_k = self.top_k if exact_filter or not user_metadata_filter else self.top_k * 4\n            search_options = {\"source_filter\": user_metadata_filter} if exact_filter else {}\n            results = await backend.similarity_search(\n                query=self.search_query or \"\",\n                k=search_k,\n                with_scores=use_scores,\n                **search_options,\n            )\n            if user_metadata_filter and not exact_filter:\n                results = [\n                    (doc, score) for doc, score in results if _chunk_matches_filter(doc.metadata, user_metadata_filter)\n                ][: self.top_k]\n\n            embeddings_by_key: dict[tuple[str, str], list[float]] = {}\n            if self.include_embeddings and results:\n                # Join each retrieved chunk to its stored embedding. We key on\n                # ``_id`` when present and fall back to page content otherwise,\n                # so KBs populated by direct file upload — whose chunks carry no\n                # ``_id`` — still resolve. See ``_embedding_match_key``.\n                wanted_keys = {_embedding_match_key(doc.page_content, doc.metadata) for doc, _score in results}\n                async with aclosing(backend.iter_documents(include_embeddings=True)) as batches:\n                    async for batch in batches:\n                        for entry in batch:\n                            if entry.embedding is None:\n                                continue\n                            key = _embedding_match_key(entry.content, entry.metadata)\n                            if key in wanted_keys:\n                                embeddings_by_key[key] = entry.embedding\n                        if wanted_keys <= embeddings_by_key.keys():\n                            break\n\n            data_list: list[Data] = []\n            for doc, score in results:\n                kwargs: dict[str, Any] = {\"content\": doc.page_content}\n                if use_scores:\n                    kwargs[\"_score\"] = backend.normalize_score(score)\n                if self.include_metadata:\n                    kwargs.update(doc.metadata)\n                if self.include_embeddings:\n                    kwargs[\"_embeddings\"] = embeddings_by_key.get(_embedding_match_key(doc.page_content, doc.metadata))\n                data_list.append(Data(**kwargs))\n\n            return DataFrame(data=data_list)\n        finally:\n            await backend.teardown()\n\n\ndef _embedding_match_key(content: str, metadata: dict[str, Any] | None) -> tuple[str, str]:\n    \"\"\"Build a stable key for aligning a retrieved chunk with its stored embedding.\n\n    Embeddings are gathered separately (via ``iter_documents``) and then joined\n    back onto the search results. Component-driven ingestion stamps a\n    content-hash ``_id`` on every chunk, but direct file-upload ingestion\n    (``KBIngestionHelper.perform_ingestion``) does not. Keying the join purely on\n    ``_id`` therefore left every upload-populated KB with ``_embeddings: None``.\n\n    We prefer ``_id`` when present (so legitimately distinct chunks that happen to\n    share text aren't collapsed) and fall back to the chunk's page content\n    otherwise. The content fallback is exact for the embedding use case: two\n    chunks with identical text necessarily share the same embedding vector. The\n    leading ``\"id\"`` / ``\"content\"`` tag namespaces the two key spaces so a mixed\n    KB (some chunks with ``_id``, some without) never cross-matches.\n    \"\"\"\n    doc_id = metadata.get(\"_id\") if metadata else None\n    if doc_id:\n        return (\"id\", str(doc_id))\n    return (\"content\", content or \"\")\n\n\ndef _parse_metadata_filter(raw: str | None) -> dict[str, list[str]]:\n    \"\"\"Decode the ``metadata_filter`` input into a {key: [values]} map.\n\n    Empty or malformed input maps to an empty filter so retrieval falls back\n    to the unfiltered path. JSON errors are swallowed here rather than raised:\n    surfacing component-config errors at the canvas node would break a flow\n    run for what is meant to be an optional refinement.\n    \"\"\"\n    if not raw:\n        return {}\n    text = raw.strip() if isinstance(raw, str) else raw\n    if not text:\n        return {}\n    try:\n        decoded = json.loads(text)\n    except (TypeError, json.JSONDecodeError):\n        logger.warning(\"KnowledgeComponent: metadata_filter is not valid JSON; ignoring filter.\")\n        return {}\n    if not isinstance(decoded, dict):\n        logger.warning(\"KnowledgeComponent: metadata_filter must be a JSON object; ignoring filter.\")\n        return {}\n    result: dict[str, list[str]] = {}\n    for key, value in decoded.items():\n        if not isinstance(key, str):\n            continue\n        if isinstance(value, list):\n            result[key] = [str(entry) for entry in value]\n        else:\n            result[key] = [str(value)]\n    return result\n\n\ndef _chunk_matches_filter(metadata: dict[str, Any] | None, filt: dict[str, list[str]]) -> bool:\n    \"\"\"AND across keys, OR within key values, mirroring the chunks endpoint.\"\"\"\n    if not filt:\n        return True\n    if not metadata:\n        return False\n    raw = metadata.get(\"source_metadata\")\n    if not raw:\n        return False\n    try:\n        stored = json.loads(raw) if isinstance(raw, str) else raw\n    except json.JSONDecodeError:\n        return False\n    if not isinstance(stored, dict):\n        return False\n    for key, expected_values in filt.items():\n        actual = stored.get(key)\n        if actual is None:\n            return False\n        actual_set = {str(entry) for entry in actual} if isinstance(actual, list) else {str(actual)}\n        if not actual_set & set(expected_values):\n            return False\n    return True\n"
               },
               "column_config": {
                 "_input_type": "TableInput",
@@ -1671,11 +1671,12 @@
             "lf_version": "1.10.1rc3",
             "metadata": {
               "code_hash": "1af4deb3e747",
+              "component_class_name": "AgentComponent",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain",
-                    "version": "1.3.11"
+                    "version": "1.3.14"
                   },
                   {
                     "name": "langgraph",
@@ -1687,13 +1688,12 @@
                   },
                   {
                     "name": "langchain_core",
-                    "version": "1.4.8"
+                    "version": "1.5.1"
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.models_and_agents.agent.AgentComponent",
-              "component_class_name": "AgentComponent"
+              "module": "lfx.components.models_and_agents.agent.AgentComponent"
             },
             "minimized": false,
             "output_types": [],
@@ -1843,7 +1843,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the OpenXFlow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
+                "value": "from __future__ import annotations\n\nimport uuid\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import (\n    HumanInTheLoopMiddleware,\n    ModelCallLimitMiddleware,\n    ToolRetryMiddleware,\n)\nfrom langgraph.types import Command\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n    adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n    build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n    WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n    SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.tool_approval import ToolApprovalMixin\nfrom lfx.components.models_and_agents.agent_helpers.tool_call_id_middleware import ToolCallIDMiddleware\nfrom lfx.components.models_and_agents.memory import MemoryComponent, _safe_graph_user_id, aget_agent_chat_history\n\nif TYPE_CHECKING:\n    from collections.abc import Awaitable, Callable\n\n    from langchain_core.tools import Tool\n\n    from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import AgentPausedError, ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n    get_language_model_options,\n    get_llm,\n    handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\ndef _agent_base_inputs():\n    \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n    `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n    inputs that are no-ops here and override info text on the inputs whose\n    semantics shifted under create_agent.\n\n    `verbose` is dropped because the create_agent event stream already surfaces\n    every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n    has nothing to toggle. Saved flows that still carry a `verbose` value just\n    ignore it on load (the schema no longer declares it).\n    \"\"\"\n    drop = {\"verbose\"}\n    overrides = {\n        \"handle_parsing_errors\": BoolInput(\n            name=\"handle_parsing_errors\",\n            display_name=\"Handle Parse Errors\",\n            value=True,\n            advanced=True,\n            info=(\n                \"Adds tool-execution retry as a safety net. `create_agent` already \"\n                \"feeds tool-call validation errors back to the LLM automatically; \"\n                \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n                \"runtime failures are retried (max 2 retries).\"\n            ),\n        ),\n        \"max_iterations\": IntInput(\n            name=\"max_iterations\",\n            display_name=\"Max Iterations\",\n            value=15,\n            advanced=True,\n            range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n            info=(\n                \"Maximum number of model calls the agent can make before stopping \"\n                \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n                \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n            ),\n        ),\n    }\n    return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n    \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n    if isinstance(value, str):\n        return value\n    text = getattr(value, \"text\", None)\n    if isinstance(text, str):\n        return text\n    content = getattr(value, \"content\", None)\n    if isinstance(content, str):\n        return content\n    return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n    \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n    Used during the structured-output prompt fallback: run_agent streams the agent's\n    final answer through self.send_message (correct for message_response), but in\n    json_response the orchestrator parses that text into structured Data which the\n    downstream Chat Output emits — leaving the original emission in place produces a\n    duplicate message in the playground. The original method is always restored on exit,\n    even when the wrapped call raises.\n    \"\"\"\n    original = component.send_message\n\n    async def _noop(message, *_args, **_kwargs):\n        return message\n\n    component.send_message = _noop\n    try:\n        yield\n    finally:\n        component.send_message = original\n\n\nclass AgentComponent(ToolApprovalMixin, ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        ModelInput(\n            name=\"model\",\n            display_name=\"Language Model\",\n            info=\"Select your model provider\",\n            real_time_refresh=True,\n            required=True,\n            # Agents require tool calling — the filter is honored by\n            # ``handle_model_input_update`` so models that can't run with\n            # tools never reach the picker (and any saved selection that\n            # no longer satisfies the constraint is auto-replaced).\n            filters={\"tool_calling\": True},\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            combobox=True,\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        StrInput(\n            name=\"openai_compatible_base_url\",\n            display_name=\"OpenAI-Compatible API URL\",\n            info=\"Overrides the configured API endpoint for OpenAI-compatible providers.\",\n            show=False,\n            real_time_refresh=True,\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=(\n                \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n                \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n            ),\n            value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n            advanced=False,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            info=\"Maximum number of tokens to generate. Set to 0 for no explicit limit. Field name varies by provider.\",\n            advanced=True,\n            range_spec=RangeSpec(min=0, max=128000, step=1, step_type=\"int\"),\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *_agent_base_inputs(),\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=STREAM_INFO_TEXT,\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n        BoolInput(\n            name=\"add_calculator_tool\",\n            display_name=\"Calculator\",\n            advanced=True,\n            info=(\n                \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n                \"(safe: only +, -, *, /, ** operators via AST).\"\n            ),\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(\n            name=\"structured_response\",\n            display_name=\"Structured Response\",\n            method=\"json_response\",\n            types=[\"Data\"],\n        ),\n    ]\n\n    async def _additional_model_provider_policy_ids(self, purpose, parameters=None) -> tuple[str, ...]:\n        \"\"\"Gate the provider selector retained by legacy Agent/ALTK flows.\"\"\"\n        _ = purpose\n        from lfx.base.models.provider_registry import resolve_provider_id\n\n        effective_parameters = parameters if isinstance(parameters, Mapping) else getattr(self, \"_parameters\", None)\n        if not isinstance(effective_parameters, Mapping):\n            effective_parameters = {}\n        selected_model = effective_parameters.get(\"model\", getattr(self, \"model\", None))\n        if selected_model:\n            # The shared Component hook already handles a selected ModelInput;\n            # connected model objects are gated by their upstream vertex.\n            return ()\n        legacy_provider = effective_parameters.get(\"agent_llm\", getattr(self, \"agent_llm\", None))\n        if not isinstance(legacy_provider, str) or not legacy_provider.strip() or legacy_provider == \"Custom\":\n            return ()\n        return (resolve_provider_id(legacy_provider),)\n\n    async def _filter_legacy_provider_options(self, build_config: Mapping[str, Any]) -> None:\n        \"\"\"Filter ALTK/legacy Agent provider choices through the active scope.\"\"\"\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose, aresolve_model_provider_policy\n\n        provider_field = build_config.get(\"agent_llm\")\n        if not isinstance(provider_field, dict):\n            return\n        options = provider_field.get(\"options\")\n        if not isinstance(options, list):\n            return\n        candidates = [option for option in options if isinstance(option, str) and option != \"Custom\"]\n        if not candidates:\n            return\n        snapshot = await aresolve_model_provider_policy(\n            user_id=self.user_id,\n            providers=candidates,\n            purpose=ModelProviderPolicyPurpose.CONFIGURE,\n        )\n        allowed = set(snapshot.filter(candidates))\n        keep_indexes = [index for index, option in enumerate(options) if option == \"Custom\" or option in allowed]\n        provider_field[\"options\"] = [options[index] for index in keep_indexes]\n        metadata = provider_field.get(\"options_metadata\")\n        if isinstance(metadata, list) and len(metadata) == len(options):\n            provider_field[\"options_metadata\"] = [metadata[index] for index in keep_indexes]\n\n    def _resolve_selected_model(self):\n        \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return self.model\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            return self.model\n\n        legacy_provider = getattr(self, \"agent_llm\", None)\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if not legacy_provider or not legacy_model_name:\n            return self.model\n\n        options = get_language_model_options(user_id=self.user_id)\n        for option in options:\n            if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n                return [option]\n\n        return [\n            {\n                \"name\": legacy_model_name,\n                \"provider\": legacy_provider,\n                \"metadata\": {},\n            }\n        ]\n\n    def _get_max_tokens_value(self):\n        \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n        val = getattr(self, \"max_tokens\", None)\n        if val in {\"\", 0}:\n            return None\n        return val\n\n    def _get_llm(self):\n        \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n        Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n        emits ``on_chat_model_stream`` chunks when the underlying chat model is\n        instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n        ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n        kept in the UI for backwards compatibility but is intentionally ignored here.\n        Without ``stream=True``, the chat model accumulates the whole response and\n        only emits ``on_chat_model_end``, silently disabling the Playground's live-\n        typing view and breaking the streaming contract on the /events surface.\n        \"\"\"\n        return get_llm(\n            model=self.model,\n            user_id=self.user_id,\n            api_key=getattr(self, \"api_key\", None),\n            stream=True,\n            max_tokens=self._get_max_tokens_value(),\n            watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n            watsonx_project_id=getattr(self, \"project_id\", None),\n            openai_compatible_base_url=getattr(self, \"openai_compatible_base_url\", None),\n            overrides=getattr(self, \"_model_overrides\", None),\n        )\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        from langchain_core.tools import StructuredTool\n\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        await self.arequire_model_provider_policy(ModelProviderPolicyPurpose.USE)\n\n        selected_model = self._resolve_selected_model()\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            is_connected_model = isinstance(selected_model, BaseLanguageModel)\n        except ImportError:\n            is_connected_model = False\n\n        if not is_connected_model:\n            validate_model_selection(selected_model)\n\n        # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n        self.model = selected_model\n        llm_model = self._get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n                self.tools.append(current_date_tool)\n\n        # Add calculator tool if enabled (zero-config arithmetic)\n        if getattr(self, \"add_calculator_tool\", False):\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n            if not isinstance(calculator_tool, StructuredTool):\n                msg = \"CalculatorComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            # Skip if an externally-connected tool already provides the same name.\n            # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n            if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n                self.tools.append(calculator_tool)\n\n        # Set shared callbacks for tracing the tools used by the agent\n        self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n        return llm_model, self.chat_history, self.tools\n\n    def _get_resolved_model_name(self) -> str:\n        \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n        try:\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(self.model, BaseLanguageModel):\n                return type(self.model).__name__\n        except ImportError:\n            pass\n\n        if isinstance(self.model, list) and self.model:\n            first = self.model[0]\n            if isinstance(first, dict):\n                name = first.get(\"name\")\n                if isinstance(name, str) and name:\n                    return name\n\n        legacy_model_name = getattr(self, \"model_name\", None)\n        if isinstance(legacy_model_name, str) and legacy_model_name:\n            return legacy_model_name\n        return \"\"\n\n    def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n        \"\"\"Replace known env placeholders in the system prompt.\n\n        Handles {current_date}, {model_name}, and {optional_user_context} (the\n        last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n        is currently unused at the AgentComponent layer, so it resolves to \"\").\n        Uses str.replace (not str.format) so user prompts containing literal\n        braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n        `system_prompt` is a connectable MultilineInput, so the value can arrive\n        as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n        a raw Message has no `.replace` and used to crash the agent build.\n        \"\"\"\n        if prompt is None:\n            return None\n        prompt = _extract_text_content(prompt)\n        if not prompt:\n            return prompt\n        replacements = {\n            \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n            \"{model_name}\": self._get_resolved_model_name(),\n            \"{optional_user_context}\": \"\",\n        }\n        for placeholder, value in replacements.items():\n            prompt = prompt.replace(placeholder, value)\n        return prompt\n\n    def create_agent_runnable(self, *, allow_interrupts: bool = True):\n        \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n        Replaces the legacy `AgentExecutor` runnable inherited from\n        `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n        openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n        runs on the new graph API.\n\n        `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n        translated to LangGraph middleware. Without that translation those user inputs\n        would silently become no-ops on the new API.\n\n        Provider notes:\n        - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n          handles tool_choice correctly. The legacy `create_granite_agent` path was\n          dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n          API now rejects.\n        - Ollama and other small/local models often emit malformed tool args. The\n          ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n          catches Pydantic ValidationErrors from bad args and feeds the error back\n          to the LLM as a retry signal, so the agent recovers gracefully.\n        \"\"\"\n        llm = self._get_llm()\n        tools = self.tools or []\n\n        # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n        # an LLM that doesn't support tool calling fails on the first user message\n        # instead of when the user wires up the component, which is a much worse UX.\n        # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n        # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n        # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n        # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n        # (signature mismatch). Treat all three as the same UX failure.\n        if tools:\n            try:\n                llm.bind_tools(tools)\n            except (NotImplementedError, AttributeError, TypeError) as exc:\n                # Include the underlying error so a broken tool schema or a\n                # provider implementation bug is not silently disguised as a\n                # \"model can't call tools\" UX error.\n                msg = (\n                    f\"{self.display_name} does not support tool calling, \"\n                    \"or one of the connected tools failed to bind. \"\n                    \"Please connect a tool-calling capable language model and \"\n                    f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n                )\n                raise NotImplementedError(msg) from exc\n\n        middleware = self._build_middleware(llm, allow_interrupts=allow_interrupts)\n        checkpointer = self._build_agent_checkpointer() if allow_interrupts else None\n        return create_agent(\n            model=llm,\n            tools=tools,\n            system_prompt=self.system_prompt or \"\",\n            middleware=middleware or None,\n            checkpointer=checkpointer,\n        )\n\n    def _compute_recursion_limit(self) -> int:\n        \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n        Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n        saved 0 or negative value cannot under-cap the graph below one full\n        iteration.\n\n        A tool-calling cycle visits the model-call limiter's before_model and\n        after_model nodes, the model, and tools. When any tool requires approval,\n        HumanInTheLoopMiddleware adds another after_model node to every cycle,\n        even if the model repeatedly calls an ungated tool. Leave transition\n        room so the model-call limiter can end the run first.\n        \"\"\"\n        raw = getattr(self, \"max_iterations\", None)\n        run_limit = max(1, int(raw)) if raw is not None else 15\n        steps_per_cycle = 4 + bool(self._gated_interrupt_on())\n        return run_limit * steps_per_cycle + 10\n\n    def _build_middleware(self, llm: Any, *, allow_interrupts: bool = True) -> list:\n        # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n        # because some providers do credential resolution / client instantiation\n        # lazily on each call. The caller — `create_agent_runnable` — already\n        # resolved it once for `bind_tools`, so reuse that instance here.\n        middleware: list = []\n        # LangChain accepts missing IDs on AIMessage.tool_calls, but LangGraph's\n        # invalid-call path and ToolRetryMiddleware both require a string when\n        # they construct an error ToolMessage. Normalize at the model boundary\n        # so either recovery path can return the error to the model instead of\n        # crashing the flow.\n        if self.tools:\n            middleware.append(ToolCallIDMiddleware())\n        max_iterations = getattr(self, \"max_iterations\", None)\n        if max_iterations is not None:\n            # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n            # 0 or negative value (falsy) must NOT silently drop the limiter and\n            # allow an unbounded model/tool loop — clamp it to a real minimum.\n            run_limit = max(1, int(max_iterations))\n            middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n        # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n        # it on a no-tools agent inflates the compiled graph and adds per-invocation\n        # middleware overhead for nothing, which is a measurable contributor to\n        # trivial-prompt latency (QA UI-003).\n        if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n            middleware.append(ToolRetryMiddleware(max_retries=2))\n        # WatsonX models have two known platform quirks; both still reproduce on\n        # the current API, so we keep the protections from the legacy\n        # `create_granite_agent` path.\n        # 1. Multi-tool-call assistant turns are rejected (\"This model only\n        #    supports single tool-calls at once!\"). Clamp to one per turn.\n        # 2. Tool args occasionally come back as literal placeholder strings\n        #    (e.g. `<result-from-search>`). Re-invoke once with a corrective\n        #    SystemMessage.\n        # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n        # applied to the final response, including any corrective re-invoke\n        # produced by WatsonXPlaceholderMiddleware.\n        if is_watsonx_model(llm):\n            middleware.append(SingleToolCallMiddleware())\n            middleware.append(WatsonXPlaceholderMiddleware())\n        # Human-in-the-loop: attach only when a tool is gated AND interrupts are allowed\n        # (the structured-output path disables them), keeping ungated flows unchanged.\n        interrupt_on = self._gated_interrupt_on() if allow_interrupts else {}\n        if interrupt_on:\n            middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))\n        return middleware\n\n    async def run_agent(self, agent) -> Message:\n        \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n        Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n        `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n        wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n        `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n        (in `lfx.base.agents.events`) can be reused unchanged.\n        \"\"\"\n        messages = build_initial_messages(\n            input_value=self.input_value,\n            chat_history=getattr(self, \"chat_history\", None),\n        )\n        input_dict = {\"messages\": messages}\n\n        agent_message = self._build_initial_agent_message()\n        token_usage_handler = TokenUsageCallbackHandler()\n\n        # Stream tokens to the event manager when running inside the Langflow runtime.\n        # This is what powers the live-typing view in the chat UI.\n        on_token_callback: OnTokenFunctionType | None = None\n        if getattr(self, \"_event_manager\", None):\n            on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n        # Align LangGraph's `recursion_limit` with `max_iterations` so the\n        # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n        # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n        # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n        # Include the extra after_model node when tool approval is configured.\n        recursion_limit = self._compute_recursion_limit()\n\n        agent_config: dict[str, Any] = {\n            \"callbacks\": [\n                AgentAsyncHandler(self.log),\n                token_usage_handler,\n                *self._get_shared_callbacks(),\n            ],\n            \"recursion_limit\": recursion_limit,\n        }\n        # The durable checkpointer keys on the per-run thread_id; it must be in the\n        # astream_events config (not just create_agent) for both initial run and resume.\n        thread_id = self._agent_thread_id()\n        get_pending_interrupt = None\n        stream_input: Any = input_dict\n        # A checkpointer is only present when the agent was built with interrupts enabled\n        # (the structured-output path builds without one); never probe its state otherwise.\n        interrupts_enabled = getattr(agent, \"checkpointer\", None) is not None\n        if interrupts_enabled and thread_id and self._gated_interrupt_on():\n            agent_config[\"configurable\"] = {\"thread_id\": thread_id}\n            get_pending_interrupt = self._pending_interrupt_getter(agent, agent_config)\n            if self._has_candidate_decision(thread_id):\n                # The injected decision must match the pending interrupt's nonce, so read\n                # the interrupt first; a matched decision resumes the checkpointed thread.\n                value, interrupt_id = await self._read_pending_interrupt(agent, agent_config)\n                decision = self._injected_agent_decision(thread_id, interrupt_id)\n                if decision is not None:\n                    action_requests = (value or {}).get(\"action_requests\") or []\n                    stream_input = Command(\n                        resume={\"decisions\": self._build_resume_decisions(decision, action_requests)}\n                    )\n        stream = adapt_graph_events_to_executor_shape(\n            agent.astream_events(stream_input, config=agent_config, version=\"v2\")\n        )\n        try:\n            result = await process_agent_events(\n                stream,\n                agent_message,\n                cast(\"SendMessageFunctionType\", self.send_message),\n                on_token_callback,\n                get_pending_interrupt=get_pending_interrupt,\n            )\n        except AgentPausedError as e:\n            # Why: retract the empty partial bubble (leaks as \"[]\"); the HITL card supersedes it, resume re-emits it.\n            paused_message = e.agent_message or agent_message\n            msg_id = paused_message.get_id()\n            if msg_id:\n                await delete_message(id_=msg_id)\n            await self._send_message_event(paused_message, category=\"remove_message\")\n            return self._suspend_for_tool_approval(e.request, paused_message)\n        except ExceptionWithMessageError as e:\n            # Drop the half-stored partial message from the DB (only if it was\n            # actually persisted) and tell the frontend to remove the stale bubble.\n            if hasattr(e, \"agent_message\"):\n                msg_id = e.agent_message.get_id()\n                if msg_id:\n                    await delete_message(id_=msg_id)\n                await self._send_message_event(e.agent_message, category=\"remove_message\")\n            # exception(), not error(f\"...{e}\"): this class's str() interpolates the model's\n            # partial completion, and passing the exception rather than formatting it is what\n            # gives the exported record an error.type to triage on.\n            logger.exception(\"Agent run failed after a partial message was emitted\")\n            raise\n\n        usage_data = token_usage_handler.get_usage()\n        if usage_data:\n            self._token_usage = usage_data\n            result.properties.usage = usage_data\n            # Only round-trip the DB when the message was stored (Chat Output wired).\n            # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n            # then would create a phantom row.\n            if result.get_id():\n                stored_result = await self._update_stored_message(result)\n                await self._send_message_event(stored_result)\n                result = stored_result\n\n        self.status = result\n        return result\n\n    def _build_initial_agent_message(self) -> Message:\n        \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n        if hasattr(self, \"graph\"):\n            session_id = self.graph.session_id\n        elif hasattr(self, \"_session_id\"):\n            session_id = self._session_id\n        else:\n            session_id = None\n\n        sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n        return Message(\n            sender=MESSAGE_SENDER_AI,\n            sender_name=sender_name,\n            properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n            # `text=\"\"` sentinel so MessageTable's no_content check accepts\n            # an in-flight agent message whose content_blocks haven't been\n            # populated yet. Mirrors ChatInput's convention.\n            text=\"\",\n            # Flat chronological event log; see lfx.base.agents.events.\n            content_blocks=[],\n            session_id=session_id or uuid.uuid4(),\n        )\n\n    def _selected_model_remediation_context(self) -> tuple[str | None, str | None, Any | None]:\n        \"\"\"Return provider/name plus a connected model target, when present.\"\"\"\n        try:\n            selected = self._resolve_selected_model()\n            if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n                return selected[0].get(\"provider\"), selected[0].get(\"name\"), None\n\n            from langchain_core.language_models import BaseLanguageModel\n\n            if isinstance(selected, BaseLanguageModel):\n                model_name = None\n                for attr in (\"model_name\", \"model\", \"model_id\"):\n                    value = getattr(selected, attr, None)\n                    if isinstance(value, str) and value:\n                        model_name = value\n                        break\n                return self._connected_model_provider(selected), model_name, selected\n        except (AttributeError, TypeError, ValueError, KeyError, ImportError):\n            pass\n        return None, None, None\n\n    def _connected_model_provider(self, model: Any) -> str | None:\n        \"\"\"Resolve a connected model's provider from the source component.\n\n        Runtime model classes are not provider identities: OpenAI, OpenRouter,\n        and compatible endpoints can all produce ``ChatOpenAI``. The incoming\n        model edge preserves the source component, so prefer its explicit\n        provider override or selected-model metadata, then its provider display\n        name. If the Agent is used without graph provenance, leave the provider\n        unknown so provider-scoped remediations remain disabled.\n        \"\"\"\n        vertex = getattr(self, \"_vertex\", None)\n        if vertex is None:\n            return None\n        source_id = vertex.get_incoming_edge_by_target_param(\"model\")\n        if not source_id:\n            return None\n        source = vertex.graph.get_vertex(source_id)\n        component = getattr(source, \"custom_component\", None)\n        if component is None:\n            return None\n\n        candidate = getattr(component, \"provider\", None)\n\n        if not isinstance(candidate, str) or not candidate:\n            selected_model = getattr(component, \"model\", None)\n            if isinstance(selected_model, list) and selected_model and isinstance(selected_model[0], dict):\n                candidate = selected_model[0].get(\"provider\")\n\n        if not isinstance(candidate, str) or not candidate:\n            candidate = getattr(component, \"display_name\", None)\n        if not isinstance(candidate, str) or not candidate:\n            return None\n\n        from lfx.base.models.unified_models.provider_queries import get_model_provider_metadata\n\n        provider_metadata = get_model_provider_metadata().get(candidate, {})\n        expected_class = provider_metadata.get(\"mapping\", {}).get(\"model_class\")\n        model_classes = {base.__name__ for base in type(model).__mro__}\n        return candidate if expected_class in model_classes else None\n\n    async def _run_agent_with_model_remediation(\n        self,\n        run_once: Callable[[], Awaitable[Message]],\n    ) -> Message:\n        \"\"\"Run one Agent operation, retrying safe provider-validation failures.\n\n        Registered remediations must identify request-validation failures that\n        occur before tool execution. A failure that can happen after a tool runs\n        must instead retry at the model-call boundary to avoid repeating tool\n        side effects.\n        \"\"\"\n        from lfx.base.models.model_remediation import apply_overrides_to_model, find_remediation, remember\n\n        provider, model_name, connected_model = self._selected_model_remediation_context()\n        applied: set[str] = set()\n        while True:\n            try:\n                result = await run_once()\n            except (ValueError, TypeError, KeyError):\n                await logger.aexception(\"Agent run failed\")\n                raise\n            except Exception as exc:\n                # run_agent may wrap the provider error in ExceptionWithMessageError, whose\n                # str() carries the model's partial completion. Matching on it locally is fine;\n                # logging it is not, so the record below passes the exception instead.\n                error_text = f\"{exc} {getattr(exc, '__cause__', '') or ''}\"\n                remediation = find_remediation(error_text, provider, already_applied=applied)\n                if remediation is None:\n                    await logger.aexception(\"Agent run failed with no remediation available\")\n                    raise\n                if connected_model is not None and not apply_overrides_to_model(connected_model, remediation.overrides):\n                    await logger.aerror(\n                        f\"model.remediation.unapplied name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                    raise\n                applied.add(remediation.name)\n                if connected_model is None:\n                    self._model_overrides = {\n                        **(getattr(self, \"_model_overrides\", None) or {}),\n                        **remediation.overrides,\n                    }\n                else:\n                    # Connected outputs are shared objects across downstream flow\n                    # branches. The mutation is intentionally flow-scoped: sibling\n                    # branches holding this model will see the matched override too.\n                    await logger.adebug(\n                        f\"model.remediation.shared_model name={remediation.name} provider={provider} model={model_name}\"\n                    )\n                await logger.awarning(\n                    f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n                )\n            else:\n                if connected_model is None and getattr(self, \"_model_overrides\", None):\n                    remember(provider, model_name, self._model_overrides)\n                return result\n\n    async def message_response(self) -> Message:\n        async def _run_once() -> Message:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n            )\n            agent = self.create_agent_runnable()\n            return await self.run_agent(agent)\n\n        result = await self._run_agent_with_model_remediation(_run_once)\n        self._agent_result = result\n        return result\n\n    async def json_response(self) -> Data:\n        \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n        Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n        returns provider-validated JSON. When tools are attached, falls back to running the\n        agent with a schema-augmented system prompt and parsing the final message content.\n        \"\"\"\n        from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n            orchestrate_structured_output,\n        )\n\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n        except (ValueError, TypeError) as exc:\n            await logger.aexception(\"json_response.requirements_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n        injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n        format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n        output_schema = getattr(self, \"output_schema\", None) or []\n        has_tools = bool(self.tools)\n\n        async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n            first_attempt = True\n\n            async def _run_once() -> Message:\n                nonlocal first_attempt, llm_model\n                if first_attempt:\n                    first_attempt = False\n                else:\n                    llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n                self.set(\n                    llm=llm_model,\n                    tools=self.tools or [],\n                    chat_history=self.chat_history,\n                    input_value=self.input_value,\n                    system_prompt=augmented_prompt,\n                )\n                # Structured output cannot suspend mid-parse: disable tool-approval interrupts.\n                agent_runnable = self.create_agent_runnable(allow_interrupts=False)\n                return await self.run_agent(agent_runnable)\n\n            with _suppress_send_message(self):\n                result = await self._run_agent_with_model_remediation(_run_once)\n            return _extract_text_content(result)\n\n        try:\n            return await orchestrate_structured_output(\n                llm=llm_model,\n                output_schema=output_schema,\n                system_prompt=injected_system_prompt,\n                format_instructions=format_instructions,\n                input_value=_extract_text_content(self.input_value),\n                run_prompt_fallback=_run_agent_for_fallback,\n                prefer_native=not has_tools,\n            )\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as exc:\n            await logger.aexception(\"json_response.orchestration_failed\")\n            return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n    async def get_memory_data(self):\n        # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n        # cannot leak chat history across unrelated flows. See issue #13059.\n        # The helper also returns [] when n_messages == 0, preserving the\n        # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n        messages = await aget_agent_chat_history(\n            session_id=self.graph.session_id,\n            flow_id=getattr(self.graph, \"flow_id\", None),\n            context_id=self.context_id,\n            n_messages=self.n_messages,\n            user_id=_safe_graph_user_id(self),\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: list[dict],\n        field_name: str | None = None,\n    ) -> dotdict:\n        from lfx.services.model_provider_policy import ModelProviderPolicyPurpose\n\n        policy_parameters = dict(getattr(self, \"_parameters\", {}) or {})\n        if field_name:\n            policy_parameters[field_name] = field_value\n        await self.arequire_model_provider_policy(\n            ModelProviderPolicyPurpose.CONFIGURE,\n            parameters=policy_parameters,\n        )\n        # Update model options with caching (for all field changes).\n        # The tool-calling constraint lives on the ModelInput's ``filters``\n        # field (declared above); ``handle_model_input_update`` reads it\n        # and applies the filter to both the dropdown options and the\n        # sticky-default re-injection path.\n        build_config = handle_model_input_update(\n            component=self,\n            build_config=dict(build_config),\n            field_value=field_value,\n            field_name=field_name,\n        )\n        build_config = dotdict(build_config)\n\n        if field_name == \"model\":\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys. `verbose` was dropped from the input set\n            # (see `_agent_base_inputs` — the create_agent event stream already\n            # surfaces every step), so it is intentionally NOT required here.\n            # Saved flows that still carry a `verbose` value just ignore it on\n            # load.\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"model\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"add_calculator_tool\",\n                \"system_prompt\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        await self._filter_legacy_provider_options(build_config)\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            # here we do not use the shared callbacks as we are exposing the agent as a tool\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n        return tools\n"
               },
               "context_id": {
                 "_input_type": "MessageTextInput",
@@ -2466,4 +2466,4 @@
     "rag",
     "q-a"
   ]
-}
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/locales/en.json b/src/backend/base/langflow/locales/en.json
index de4d33f97b..97bc7686ab 100644
--- a/src/backend/base/langflow/locales/en.json
+++ b/src/backend/base/langflow/locales/en.json
@@ -95,7 +95,7 @@
   "components.altermetadata.outputs.data.info.d99d8586": "List of Input objects each with added Metadata",
   "components.altermetadata.outputs.dataframe.display_name.16d1c905": "Table",
   "components.altermetadata.outputs.dataframe.info.64cf6cf1": "Data objects as a DataFrame, with metadata as columns",
-  "components.altkagent.description.f5f1d3c5": "ALTK was retired in OpenXFlow 1.13.0. Replace this node with the Agent component.",
+  "components.altkagent.description.4da568f4": "ALTK was retired in OpenXFlow 1.13.0. Replace this node with the Agent component.",
   "components.altkagent.display_name.73afda4a": "ALTK Agent",
   "components.altkagent.inputs.add_calculator_tool.display_name.668b8045": "Calculator",
   "components.altkagent.inputs.add_calculator_tool.info.a45cfd4c": "If true, adds a zero-config arithmetic calculator tool to the agent (safe: only +, -, *, /, ** operators via AST).",
@@ -128,6 +128,8 @@
   "components.altkagent.inputs.model.placeholder.56590d8e": "Setup Provider",
   "components.altkagent.inputs.n_messages.display_name.3105e924": "Number of Chat History Messages",
   "components.altkagent.inputs.n_messages.info.39873fa8": "Number of chat history messages to retrieve.",
+  "components.altkagent.inputs.openai_compatible_base_url.display_name.c85ef531": "OpenAI-Compatible API URL",
+  "components.altkagent.inputs.openai_compatible_base_url.info.ccfff607": "Overrides the configured API endpoint for OpenAI-compatible providers.",
   "components.altkagent.inputs.output_schema.display_name.21e3cb67": "Output Schema",
   "components.altkagent.inputs.output_schema.info.3b7a7b80": "Schema Validation: Define the structure and data types for structured output. No validation if no output schema.",
   "components.altkagent.inputs.project_id.display_name.c7feb8db": "watsonx Project ID",
@@ -1278,6 +1280,8 @@
   "components.file.inputs.storage_location.display_name.6b1f9093": "Storage Location",
   "components.file.inputs.storage_location.info.713b4100": "Choose where to read the file from.",
   "components.file.inputs.storage_location.placeholder.3c82033f": "Select Location",
+  "components.file.inputs.text_encoding.display_name.ab5ad1dc": "Text Encoding",
+  "components.file.inputs.text_encoding.info.6a86b444": "Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.",
   "components.file.inputs.use_multithreading.display_name.a32391d0": "[Deprecated] Use Multithreading",
   "components.file.inputs.use_multithreading.info.e1193371": "Set 'Processing Concurrency' greater than 1 to enable multithreading.",
   "components.file.outputs.message.display_name.960c8537": "Raw Content",
@@ -1849,7 +1853,7 @@
   "components.jsontodata.inputs.json_string.display_name.560082ac": "JSON String",
   "components.jsontodata.inputs.json_string.info.5786aa41": "Enter a valid JSON string (object or array) to convert to a Data object or list of Data objects",
   "components.jsontodata.outputs.data.display_name.db1a21a0": "JSON",
-  "components.knowledge.description.a1a10675": "Ingest into or retrieve from an OpenXFlow knowledge base.",
+  "components.knowledge.description.87015105": "Ingest into or retrieve from an OpenXFlow knowledge base.",
   "components.knowledge.display_name.dcb3e1c0": "Knowledge",
   "components.knowledge.inputs.allow_duplicates.display_name.c5507652": "Allow Duplicates",
   "components.knowledge.inputs.allow_duplicates.info.4f0b36ee": "Allow duplicate rows in the knowledge base",
@@ -1910,7 +1914,7 @@
   "components.knowledgebase.inputs.top_k.info.dc908edc": "Number of top results to return from the knowledge base.",
   "components.knowledgebase.outputs.retrieve_data.display_name.219c4a6c": "Results",
   "components.knowledgebase.outputs.retrieve_data.info.791a5874": "Returns the data from the selected knowledge base.",
-  "components.knowledgeingestion.description.4a211a27": "Create or update knowledge in OpenXFlow.",
+  "components.knowledgeingestion.description.4248eab8": "Create or update knowledge in OpenXFlow.",
   "components.knowledgeingestion.display_name.89439d2d": "Knowledge Ingestion",
   "components.knowledgeingestion.inputs.allow_duplicates.display_name.c5507652": "Allow Duplicates",
   "components.knowledgeingestion.inputs.allow_duplicates.info.4f0b36ee": "Allow duplicate rows in the knowledge base",
@@ -2073,12 +2077,12 @@
   "components.mcptools.inputs.verify_ssl.display_name.e9ee31b8": "Verify SSL Certificate",
   "components.mcptools.inputs.verify_ssl.info.63deba67": "Enable SSL certificate verification for HTTPS connections. Disable only for development/testing with self-signed certificates.",
   "components.mcptools.outputs.response.display_name.9061383b": "Response",
-  "components.memory.description.31449424": "Stores or retrieves stored chat messages from OpenXFlow tables or an external memory.",
+  "components.memory.description.5e6e6979": "Stores or retrieves stored chat messages from OpenXFlow tables or an external memory.",
   "components.memory.display_name.64169537": "Message History",
   "components.memory.inputs.context_id.display_name.2051cde7": "Context ID",
   "components.memory.inputs.context_id.info.55bf9c83": "The context ID of the chat. Adds an extra layer to the local memory.",
   "components.memory.inputs.memory.display_name.c7a62e64": "External Memory",
-  "components.memory.inputs.memory.info.c2d79141": "Retrieve messages from an external memory. If empty, it will use the OpenXFlow tables.",
+  "components.memory.inputs.memory.info.8d537dfe": "Retrieve messages from an external memory. If empty, it will use the OpenXFlow tables.",
   "components.memory.inputs.message.display_name.2f77668a": "Message",
   "components.memory.inputs.message.info.212d7fd0": "The chat message to be stored.",
   "components.memory.inputs.mode.display_name.5e23ec6a": "Mode",
@@ -3059,7 +3063,7 @@
   "components.slackonmessagetrigger.inputs.channels.display_name.847bd826": "Channel IDs",
   "components.slackonmessagetrigger.inputs.channels.info.d3adb3e5": "Only fire for these conversations, by ID (for example C0123456789, from the channel's details). Leave empty for every conversation the app can see.",
   "components.slackonmessagetrigger.inputs.connection.display_name.335f10dc": "Slack Connection",
-  "components.slackonmessagetrigger.inputs.connection.info.3126fcb2": "Either a Slack app installation (hosted, or self-managed with a public URL: events arrive through the Slack Events API) or an app-level token connection (OpenXFlow Desktop, or an instance Slack cannot reach: events arrive over Socket Mode). Required before the trigger can be armed.",
+  "components.slackonmessagetrigger.inputs.connection.info.2be79dca": "Either a Slack app installation (hosted, or self-managed with a public URL: events arrive through the Slack Events API) or an app-level token connection (OpenXFlow Desktop, or an instance Slack cannot reach: events arrive over Socket Mode). Required before the trigger can be armed.",
   "components.slackonmessagetrigger.inputs.conversation_types.display_name.9a6aa1a0": "Conversation types",
   "components.slackonmessagetrigger.inputs.conversation_types.info.802d162a": "channel: public channels. group: private channels. im: direct messages with the app. mpim: group direct messages.",
   "components.slackonmessagetrigger.inputs.event_payload.display_name.4e1f49a9": "Event",
@@ -3079,7 +3083,7 @@
   "components.slackonreactiontrigger.inputs.channels.display_name.847bd826": "Channel IDs",
   "components.slackonreactiontrigger.inputs.channels.info.d3adb3e5": "Only fire for these conversations, by ID (for example C0123456789, from the channel's details). Leave empty for every conversation the app can see.",
   "components.slackonreactiontrigger.inputs.connection.display_name.335f10dc": "Slack Connection",
-  "components.slackonreactiontrigger.inputs.connection.info.3126fcb2": "Either a Slack app installation (hosted, or self-managed with a public URL: events arrive through the Slack Events API) or an app-level token connection (OpenXFlow Desktop, or an instance Slack cannot reach: events arrive over Socket Mode). Required before the trigger can be armed.",
+  "components.slackonreactiontrigger.inputs.connection.info.2be79dca": "Either a Slack app installation (hosted, or self-managed with a public URL: events arrive through the Slack Events API) or an app-level token connection (OpenXFlow Desktop, or an instance Slack cannot reach: events arrive over Socket Mode). Required before the trigger can be armed.",
   "components.slackonreactiontrigger.inputs.emoji.display_name.61ad8976": "Emoji",
   "components.slackonreactiontrigger.inputs.emoji.info.4c80a33d": "Emoji names without colons, for example 'rocket'. Leave empty for any reaction.",
   "components.slackonreactiontrigger.inputs.event_payload.display_name.4e1f49a9": "Event",
@@ -3250,10 +3254,10 @@
   "components.sqlgenerator.inputs.top_k.display_name.74fc1150": "Top K",
   "components.sqlgenerator.inputs.top_k.info.481a29ee": "The number of results per select statement to return.",
   "components.sqlgenerator.outputs.text.display_name.2f77668a": "Message",
-  "components.storemessage.description.786ee729": "Stores a chat message or text into OpenXFlow tables or an external memory.",
+  "components.storemessage.description.ab58b04c": "Stores a chat message or text into OpenXFlow tables or an external memory.",
   "components.storemessage.display_name.a8c1d3a3": "Message Store",
   "components.storemessage.inputs.memory.display_name.c7a62e64": "External Memory",
-  "components.storemessage.inputs.memory.info.1bd5d94f": "The external memory to store the message. If empty, it will use the OpenXFlow tables.",
+  "components.storemessage.inputs.memory.info.bdde8480": "The external memory to store the message. If empty, it will use the OpenXFlow tables.",
   "components.storemessage.inputs.message.display_name.2f77668a": "Message",
   "components.storemessage.inputs.message.info.212d7fd0": "The chat message to be stored.",
   "components.storemessage.inputs.sender.display_name.c299f9b8": "Sender",
@@ -3671,13 +3675,13 @@
   "starter_flows.twitter_thread_generator.name": "Twitter Thread Generator",
   "starter_flows.vector_store_rag.description": "Load your data for chat context with Retrieval Augmented Generation.",
   "starter_flows.vector_store_rag.name": "Vector Store RAG",
-  "template_notes.basic_prompting.bd8ff52b": "# 📖 README\nThis template demonstrates a standard chat flow with additional instructions provided by a prompt. Prompts provide instructions and inputs for a Large Language Model (LLM) beyond the standard user-provided chat input. In this example, the prompt describes the LLM's role and persona.\n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Open the **Playground** to start the chat and run the flow.\n\n## Next steps\nChange the prompt template, model, or model settings, such as **Temperature**, and then see how the responses change with these different inputs.\n💡 Some component settings are hidden by default; to view all settings click **Controls** in each component's header menu.\n💡 You can use curly braces to create variables in your template, such as `{variable}`. These can be populated from other components, with OpenXFlow global variables, or at runtime.",
   "template_notes.basic_prompting.cd87cff6": "### Configure your Model Provider",
+  "template_notes.basic_prompting.ee8855e9": "# 📖 README\nThis template demonstrates a standard chat flow with additional instructions provided by a prompt. Prompts provide instructions and inputs for a Large Language Model (LLM) beyond the standard user-provided chat input. In this example, the prompt describes the LLM's role and persona.\n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Open the **Playground** to start the chat and run the flow.\n\n## Next steps\nChange the prompt template, model, or model settings, such as **Temperature**, and then see how the responses change with these different inputs.\n💡 Some component settings are hidden by default; to view all settings click **Controls** in each component's header menu.\n💡 You can use curly braces to create variables in your template, such as `{variable}`. These can be populated from other components, with OpenXFlow global variables, or at runtime.",
   "template_notes.blog_writer.23fde7d9": "# ✍️ Blog Writer\n\nGive it a **URL** and it writes a blog post grounded in that page. An **Agent** fetches the page itself using the **URL component as a tool** (Fetch Content), then writes the article in Markdown. Rebuilt on the new Agent core component — no separate parsing step.\n\n## Setup\n1. **Agent** — pick your **Model Provider** and paste its API key (OpenAI, Anthropic, Google, Groq, etc. — any option in the Model dropdown).\n2. **URL tool** — already wired into the Agent's **Tools**; no setup needed. Keep **Depth = 1** so it fetches just the page you give it (higher depth follows links and costs more time/tokens).\n\n## Use\n1. Open the **Playground**.\n2. Paste a **source URL**, e.g. `https://docs.langflow.org/`.\n3. The Agent fetches that page through the URL tool and returns a finished blog post.\n\n> If nothing runs on an empty task, type `Write the blog post from the source URL.` in the Agent's **Input** field.\n\n## How it works\n1. **Chat Input** — you enter the source URL.\n2. **Prompt Template `{URL}`** — injects the URL into the Agent's instructions.\n3. **Agent (+ URL tool)** — calls the URL **Fetch Content** tool to retrieve the page, then writes a Markdown post grounded only in what it fetched (no invented facts).\n4. **Chat Output** — the finished blog post.",
   "template_notes.content_aggregator.cbed07a7": "# 📰 Content Aggregator\n\nSearch the news on a topic, summarize each article, and compose a clean aggregated digest - then save it to a file.\n\n## Setup\n1. **Agent** and **Batch Run** - add your LLM key on each (any provider in the Model dropdown).\n2. **Web Search** - needs no key. Set its **Search Mode** to *News* (already selected); optionally set a **Topic** (Business, Technology, ...).\n3. **Save to File** - choose where to write the digest.\n\n## How it works\n1. **Chat Input -> Web Search (News)** - finds recent articles on your topic (returns title, link, date, summary).\n2. **Batch Run** - summarizes every article in one line.\n3. **Parser -> Agent** - the summaries are formatted and the Agent curates them into a clean digest.\n4. **Chat Output -> Save to File** - the digest is shown and saved.\n\n## Run\nType a topic (e.g. \"artificial intelligence\") and run.",
-  "template_notes.custom_component_generator.b1d3ed28": "# 🧩 Custom Component Generator\n\nDescribe the component you want and an **Agent** writes an OpenXFlow custom Component (Python). It pulls OpenXFlow's reference code on demand and remembers the conversation so you can iterate.\n\n## Setup\n1. **Agent** - add your LLM key (any provider in the Model dropdown).\n2. **URL** and **Memory** tools need no key.\n\n## How it works\n1. **Chat Input → Agent** - your component request.\n2. The Agent uses the **URL** tool to fetch OpenXFlow reference code (base class, examples) when it needs the exact conventions, and **Memory** to recall earlier turns.\n3. **Agent → Chat Output** - the generated Python. Ask follow-ups to refine it.\n\n## Run\nOpen **Playground**, describe the component you need, and run.",
+  "template_notes.custom_component_generator.5073eb43": "# 🧩 Custom Component Generator\n\nDescribe the component you want and an **Agent** writes an OpenXFlow custom Component (Python). It pulls OpenXFlow's reference code on demand and remembers the conversation so you can iterate.\n\n## Setup\n1. **Agent** - add your LLM key (any provider in the Model dropdown).\n2. **URL** and **Memory** tools need no key.\n\n## How it works\n1. **Chat Input → Agent** - your component request.\n2. The Agent uses the **URL** tool to fetch OpenXFlow reference code (base class, examples) when it needs the exact conventions, and **Memory** to recall earlier turns.\n3. **Agent → Chat Output** - the generated Python. Ask follow-ups to refine it.\n\n## Run\nOpen **Playground**, describe the component you need, and run.",
   "template_notes.deep_research_agent.195c6a9d": "# 🔎 Deeply Research Agent\n\nAsk a research question and get a thorough, synthesized answer. Three Agents work together: one plans, one researches the web, and one writes up the findings.\n\n## Setup\n1. Add your LLM key on each of the three **Agents**.\n2. **Web Search** - no key needed (DuckDuckGo / Google News).\n\n## How it works\n1. **Chat Input → Planner Agent** - drafts a plan / initial take on the question.\n2. **Search Agent (+ Web Search)** - researches the topic on the web.\n3. **Synthesizer Agent** - combines the question, plan, and search results into a final answer.\n4. **→ Chat Output** - the researched answer.\n\n## Run\nOpen **Playground**, ask a research question, and run.",
-  "template_notes.document_q_a.0e21c05f": "# 📄 Document Q&A\n\nAsk questions about your documents and get answers grounded in their content,\nusing a Knowledge Base for retrieval.\n\n## Setup\n1. **Knowledge Base** — add your documents to a Knowledge Base first (via the\n   **Knowledge** module in OpenXFlow, or a separate ingestion flow). This flow\n   only **retrieves** — it does not ingest.\n2. **Knowledge (Retrieve)** — select the Knowledge Base that holds your documents.\n3. **Agent** — add your LLM key (any provider in the Model dropdown).\n\n## How it works\n1. **Chat Input → Knowledge (Retrieve)** — your question pulls the most relevant\n   passages from the Knowledge Base.\n2. **Parser → Prompt** — the retrieved passages become the context, combined with\n   your question.\n3. **Agent → Chat Output** — the Agent answers using **only** the retrieved\n   context (it won't make things up beyond your documents).\n\n## Run\nMake sure your Knowledge Base is populated, then type a question in the Playground.",
+  "template_notes.document_q_a.f7a494a0": "# 📄 Document Q&A\n\nAsk questions about your documents and get answers grounded in their content,\nusing a Knowledge Base for retrieval.\n\n## Setup\n1. **Knowledge Base** — add your documents to a Knowledge Base first (via the\n   **Knowledge** module in OpenXFlow, or a separate ingestion flow). This flow\n   only **retrieves** — it does not ingest.\n2. **Knowledge (Retrieve)** — select the Knowledge Base that holds your documents.\n3. **Agent** — add your LLM key (any provider in the Model dropdown).\n\n## How it works\n1. **Chat Input → Knowledge (Retrieve)** — your question pulls the most relevant\n   passages from the Knowledge Base.\n2. **Parser → Prompt** — the retrieved passages become the context, combined with\n   your question.\n3. **Agent → Chat Output** — the Agent answers using **only** the retrieved\n   context (it won't make things up beyond your documents).\n\n## Run\nMake sure your Knowledge Base is populated, then type a question in the Playground.",
   "template_notes.financial_report_parser.5972260b": "# 📊 Financial Report Parser (Agent)\n\nUpload a financial report and an **Agent** extracts the key metrics (Gross Profit, EBITDA, Net Income, ...) into a structured table. Rebuilt on the **Agent** core component using its built-in **structured output** - replaces the standalone Structured Output component.\n\n## Setup\n1. **File** - upload your financial report (PDF, DOCX, XLSX, TXT).\n2. **Agent** - pick your **Model Provider** + API key. Edit the **Output Schema** to change which metrics are extracted.\n\n## How it works\n1. **File → Agent** - the Agent reads the report and extracts the schema fields.\n2. **Agent (Structured Response) → Parser** - turns the extracted JSON into readable text.\n3. **Parser → Chat Output** - shows the structured metrics.\n\n## Run\nOpen **Playground**, upload the report, and run. The Agent returns only values found in the document (a field stays blank when the metric isn't present).",
   "template_notes.hybrid_search_rag.9e93986f": "# 🔎 2) Search\n\n1. **Chat Input** - your question goes to Astra DB as the semantic query.\n2. **Agent** - extracts keywords from the question for the lexical search.\n3. **Astra DB** - combines + reranks semantic and lexical, returns the top passages.\n4. **Parser -> Chat Output** - the passages as text.\n\nRun from the Playground (or ▶ on Chat Output) after the collection is populated.",
   "template_notes.hybrid_search_rag.bc1f15c7": "# 📥 1) Ingestion - run once\n\nPopulate the collection so Search has data.\n\n1. **File** - upload your document (e.g. the sample `.md`).\n2. **Split Text** - chunks it (size 1000, overlap 100).\n3. **Astra DB** - set Token + Database + Collection, then press ▶ on this Astra DB node to write the chunks (Vectorize embeds + lexically indexes on write).",
@@ -3691,7 +3695,7 @@
   "template_notes.multi_agent_flow.5a71f2d1": "# 🧩 Multi Agent flow\n\nA three-step chain of specialist Agents that turns a market trend into a development plan. Each Agent passes its result to the next.\n\n## Setup\nAdd your LLM key on each **Agent** (any provider in the Model dropdown).\n\n## How it works\n1. **Innovator** - reads the market trend and proposes a product concept.\n2. **Analyst** - evaluates the concept's market potential, feasibility, and risks.\n3. **Product Manager** - turns it into a simple development and launch plan.\n\n## Run\nType a market trend in the chat (a default one is provided) and run; the final plan appears in Chat Output.",
   "template_notes.portfolio_website_code_generator.c217664c": "# 💻 Portfolio Website Code Generator\n\nUpload your resume and an **Agent** generates a single-page portfolio website (HTML + CSS).\n\n## Setup\n1. **File** - upload your resume (PDF or TXT).\n2. **Agent** - add your LLM key (any provider in the Model dropdown).\n\n## How it works\n1. **File → Agent** - the Agent reads your resume.\n2. **Agent** - extracts your details and writes the portfolio site (header, about, experience, projects, skills, contact) as HTML/CSS.\n3. **Agent → Chat Output** - copy the generated code.\n\n## Run\nUpload your resume, open **Playground**, and run. Tip: a clearly formatted resume (headings, bullet points) produces the best result.",
   "template_notes.price_deal_finder.6de222f2": "# 💰 Price Deal Finder\n\nAn **Agent** that hunts for the best price on a product: it searches the web (and coupon news), reads listing pages, does the math, and remembers what you're tracking - returning the deals as a structured table.\n\n## Setup\n1. **Agent** - add your LLM key. Its tools are Web Search, URL, Calculator, and Memory.\n2. **Web Search** / **URL** - no key needed.\n3. **Memory** - recalls your watchlist and preferences across sessions (no key).\n\n## How it works\n1. **Chat Input → Agent** - the product you're shopping for.\n2. The Agent searches the web (and News for coupons), reads a listing with URL, computes savings with the Calculator, and checks Memory for anything you tracked before.\n3. **Agent → Chat Output** - the best deals as a structured table (retailer, price, link, notes).\n\n## Run\nOpen **Playground**, name a product, and run.",
-  "template_notes.research_translation_loop.60bb882f": "# 📖 README\nThis template translates research paper summaries on ArXiv into Portuguese and summarizes them. \n Using **OpenXFlow’s looping mechanism**, the template iterates through multiple research papers, translates them with the **OpenAI** model component, and outputs an aggregated version of all translated papers.  \n\n## Quick start\n1. Configure your **Model Provider** with your API credentials. \n2. In the **Playground**, enter a query related to a research topic (for example, “Quantum Computing Advancements”).  \n\n  The flow fetches a list of research papers from ArXiv matching the query. Each paper in the retrieved list is processed one-by-one using the OpenXFlow **Loop component**. \n\n  The abstract of each paper is translated into Portuguese by the **OpenAI** model component. \n\n Once all papers are translated, the system aggregates them into a **single structured output**.",
+  "template_notes.research_translation_loop.c0723e26": "# 📖 README\nThis template translates research paper summaries on ArXiv into Portuguese and summarizes them. \n Using **OpenXFlow’s looping mechanism**, the template iterates through multiple research papers, translates them with the **OpenAI** model component, and outputs an aggregated version of all translated papers.  \n\n## Quick start\n1. Configure your **Model Provider** with your API credentials. \n2. In the **Playground**, enter a query related to a research topic (for example, “Quantum Computing Advancements”).  \n\n  The flow fetches a list of research papers from ArXiv matching the query. Each paper in the retrieved list is processed one-by-one using the OpenXFlow **Loop component**. \n\n  The abstract of each paper is translated into Portuguese by the **OpenAI** model component. \n\n Once all papers are translated, the system aggregates them into a **single structured output**.",
   "template_notes.saas_pricing.d9213061": "# 💵 SaaS Pricing\n\nCalculate the right monthly subscription price for a software product — from costs, profit margin, and subscriber count. The Agent can also pull the numbers from your own database.\n\n## Setup\n1. **Agent** — add your LLM key. ⚠️ **Use a capable model** (e.g. a flagship GPT / Claude / Gemini Pro tier). The Agent calls the **SQL Database** tool and does multi-step math, so smaller/faster models may loop and hit the recursion limit when querying the DB.\n2. **SQL Database** *(optional)* — set your **Database URL** to let the Agent query real cost / subscriber data from your own database. Leave it unset to just type the numbers in the chat.\n3. The Agent has a built-in **calculator** and **current-date** tool.\n\n## How it works\n1. **Chat Input → Agent** — your request (e.g. \"subscription price for Beta CRM using my database\", or \"price: infra 2000, support 1000, dev 3000, margin 30%, 200 subscribers\").\n2. The Agent queries the **SQL Database** when connected, then computes the minimum price per subscriber with its built-in calculator.\n3. **Agent → Chat Output** — the recommended pricing breakdown.\n\n## Run\nOpen the **Playground**, describe the product (or name one in your connected database), and run.\n\n> Tip: when using the database, mention the table/product so the Agent queries it in one shot — and prefer a capable model to avoid tool-call loops.",
   "template_notes.seo_keyword_generator.11d10888": "# 🔍 SEO Keyword Generator\n\nGenerate SEO keywords and phrases for a topic with an **Agent**.\n\n## Setup\n1. **Agent** - add your LLM key (any provider in the Model dropdown).\n2. **Prompt Template** - set the topic / niche and tune the keyword instructions.\n\n## How it works\n1. **Prompt (instructions) → Agent (System Prompt)** - steers how keywords are generated.\n2. **Prompt (topic) → Agent (Input)** - the subject to generate keywords for.\n3. **Agent → Chat Output** - the SEO keyword list.\n\n## Run\nOpen **Playground**, set your topic in the Prompt, and run.",
   "template_notes.sequential_tasks_agents.4b51b574": "# 🔗 Sequential Tasks Agents\n\nThree agents working in sequence - each does one task and passes its result to the next.\n\n## Setup\nAdd your LLM key on each **Agent**. The **Web Search** tool needs no key.\n\n## How it works\n1. **Research Agent** (Web Search) - gathers information on your topic.\n2. **Analysis Agent** - analyzes the findings and draws conclusions.\n3. **Writer Agent** - synthesizes research + analysis into a final report.\n\n## Run\nType a topic or question in the chat and run; the final report appears in Chat Output.",
@@ -3700,14 +3704,5 @@
   "template_notes.text_sentiment_analysis.87f5729f": "# 📊 Text Sentiment Analysis\n\nUpload a text file (a review, an email, feedback, an article) and the flow analyzes its sentiment **two complementary ways** — a quick direct classification and a deeper summarize-then-analyze pass. Each result comes back as a clean JSON object.\n\n## Setup\n1. **File** — upload the text to analyze (PDF, TXT, MD, DOCX, …).\n2. **Agents** — add your LLM key on each of the three Agents (Model dropdown). No other keys needed.\n\n## What you get (two outputs)\n**1) Direct classification** — File → Agent → Chat Output\nReads the text in one pass and returns:\n- `message_category` — e.g. *Positive Feedback*, *Complaint*, *Question*\n- `message_sentiment` — *Positive* / *Negative* / *Neutral*\n\n**2) Summary & insights** — File → Agent (summarize) → Agent (analyze) → Chat Output\nFirst condenses the text, then analyzes the summary, returning:\n- `summary` — a short recap of the text\n- `action_item` — a concrete next step suggested by the content\n- `sentiment` — overall sentiment of the summarized view\n\nThe two can differ on nuanced text — a glowing review with a few complaints may read *Positive* directly but *Neutral* once distilled. Comparing them is the point.\n\n## Run\nUpload a file, open the **Playground**, and run. Swap the file to analyze different texts.\n\n## Tips\n- Clear, well-formatted text gives better results.\n- Each Agent's behavior is set in its **Agent Instructions** / the **Prompt** feeding it — edit those to change the categories, fields, or tone.",
   "template_notes.travel_planning_agents.fcc662a4": "# ✈️ Travel Planning Agents\n\nA team of three **Agents** plans a trip end to end: one researches the destination on the web, one drafts the itinerary, and one works out the budget.\n\n## Setup\nAdd your LLM key on each of the three **Agents** (Model dropdown). **Web Search**, **URL** and **Calculator** tools need no key.\n\n## How it works\n1. **Chat Input → Research Agent** (+ **Web Search**) - gathers current info about the destination.\n2. **→ Itinerary Agent** (+ **URL**) - reads relevant pages and drafts the plan.\n3. **→ Budget Agent** (+ **Calculator**) - estimates costs and finalizes the trip.\n4. **→ Chat Output** - the complete travel plan.\n\n## Run\nOpen **Playground**, describe your trip (destination, dates, budget, interests), and run.",
   "template_notes.twitter_thread_generator.7e45a5a9": "# 🐦 Twitter Thread Generator\n\nTurn a project brief into an engaging multi-tweet **thread**. Rebuilt on the new **Agent** core component.\n\n## Setup\n1. **Agent** - add your LLM key (any provider in the Model dropdown).\n2. **Prompt Template** - tune the thread rules: PROFILE_TYPE, PROFILE_DETAILS, CONTENT_GUIDELINES, TONE_AND_STYLE, OUTPUT_FORMAT, OUTPUT_LANGUAGE.\n\n## How it works\n1. **Prompt → Agent (System Prompt)** - the instructions + style guidelines steer the Agent.\n2. **Chat Input → Agent (Input)** - your project context / what the thread is about.\n3. **Agent → Chat Output** - the finished tweet or numbered thread.\n\n## Run\nOpen **Playground**, type (or keep) the context in Chat Input, and run.",
-  "template_notes.vector_store_rag.cdd6e091": "# Vector Store RAG\n\nAsk questions over a **Knowledge Base** (retrieval-augmented generation), fully **bundle-free** - the core **Knowledge** component, no external vector database. Rebuilt on the new **Agent** core component (replaces the Language Model).\n\n## Setup\n1. Point the **Knowledge** node at a populated Knowledge Base - pick one in the dropdown, or ingest documents first (e.g. via the Document Q&A flow).\n2. **Agent** - add your LLM key (any provider in the Model dropdown).\n\n## How it works\n1. **Chat Input → Knowledge (Retrieve)** - semantic search for the most relevant chunks.\n2. **Parser → Prompt** - formats the chunks as context alongside your question.\n3. **Agent → Chat Output** - answers using only that retrieved context.",
-  "components.altkagent.description.4da568f4": "ALTK was retired in OpenXFlow 1.13.0. Replace this node with the Agent component.",
-  "components.knowledge.description.87015105": "Ingest into or retrieve from an OpenXFlow knowledge base.",
-  "components.knowledgeingestion.description.4248eab8": "Create or update knowledge in OpenXFlow.",
-  "components.memory.description.5e6e6979": "Stores or retrieves stored chat messages from OpenXFlow tables or an external memory.",
-  "components.memory.inputs.memory.info.8d537dfe": "Retrieve messages from an external memory. If empty, it will use the OpenXFlow tables.",
-  "components.slackonmessagetrigger.inputs.connection.info.2be79dca": "Either a Slack app installation (hosted, or self-managed with a public URL: events arrive through the Slack Events API) or an app-level token connection (OpenXFlow Desktop, or an instance Slack cannot reach: events arrive over Socket Mode). Required before the trigger can be armed.",
-  "components.slackonreactiontrigger.inputs.connection.info.2be79dca": "Either a Slack app installation (hosted, or self-managed with a public URL: events arrive through the Slack Events API) or an app-level token connection (OpenXFlow Desktop, or an instance Slack cannot reach: events arrive over Socket Mode). Required before the trigger can be armed.",
-  "components.storemessage.description.ab58b04c": "Stores a chat message or text into OpenXFlow tables or an external memory.",
-  "components.storemessage.inputs.memory.info.bdde8480": "The external memory to store the message. If empty, it will use the OpenXFlow tables."
+  "template_notes.vector_store_rag.cdd6e091": "# Vector Store RAG\n\nAsk questions over a **Knowledge Base** (retrieval-augmented generation), fully **bundle-free** - the core **Knowledge** component, no external vector database. Rebuilt on the new **Agent** core component (replaces the Language Model).\n\n## Setup\n1. Point the **Knowledge** node at a populated Knowledge Base - pick one in the dropdown, or ingest documents first (e.g. via the Document Q&A flow).\n2. **Agent** - add your LLM key (any provider in the Model dropdown).\n\n## How it works\n1. **Chat Input → Knowledge (Retrieve)** - semantic search for the most relevant chunks.\n2. **Parser → Prompt** - formats the chunks as context alongside your question.\n3. **Agent → Chat Output** - answers using only that retrieved context."
 }
diff --git a/src/backend/base/langflow/services/telemetry_writer/service.py b/src/backend/base/langflow/services/telemetry_writer/service.py
index 1461e25100..4a61babe1f 100644
--- a/src/backend/base/langflow/services/telemetry_writer/service.py
+++ b/src/backend/base/langflow/services/telemetry_writer/service.py
@@ -37,6 +37,7 @@
 from typing import TYPE_CHECKING, Any
 from uuid import UUID
 
+import psutil
 from lfx.log.logger import logger
 from sqlalchemy import delete, select
 from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker, create_async_engine
@@ -212,16 +213,12 @@ def drain(self) -> list[dict[str, Any]]:
 
 
 def _pid_alive(pid: int) -> bool:
-    """Return True if ``pid`` corresponds to a live process."""
-    if pid <= 0:
-        return False
-    try:
-        os.kill(pid, 0)
-    except ProcessLookupError:
-        return False
-    except PermissionError:
-        return True
-    return True
+    """Check process existence without signaling or terminating the worker.
+
+    Unlike POSIX, Windows treats ``os.kill(pid, 0)`` as termination and raises
+    WinError 87 for missing PIDs. psutil handles these platform differences.
+    """
+    return pid > 0 and psutil.pid_exists(pid)
 
 
 class TelemetryWriterService(Service):
diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml
index 9a2117b230..52f00432d1 100644
--- a/src/backend/base/pyproject.toml
+++ b/src/backend/base/pyproject.toml
@@ -60,7 +60,10 @@ dependencies = [
     # Schedule triggers (TRG-2). Cron semantics live in langflow-base only; the
     # lfx component validates expressions syntactically so lfx keeps no cron dep.
     "croniter>=2.0.0,<7.0.0",
-    "pillow>=12.3.0,<13.0.0",
+    # Pillow 13 contains security fixes for crafted images but requires Python >=3.11.
+    # Preserve 3.10 installation while ensuring our Python 3.14 production image uses 13+.
+    "pillow>=12.3.0,<13.0.0; python_version < '3.11'",
+    "pillow>=13.0.0,<14.0.0; python_version >= '3.11'",
     "docstring-parser>=0.16,<1.0.0",
     "PyJWT>=2.15.1,<3.0.0",  # CVE-2026-102266, CVE-2026-102267, CVE-2026-102271, CVE-2026-102272, CVE-2026-102274
     "pandas>=2.2.3",
@@ -92,7 +95,8 @@ dependencies = [
     "pip>=26.2.1,<27.0.0",
     "setuptools>=83.0.0,<84.0.0",
     "nanoid>=2.0.0,<3.0.0",
-    "filelock>=3.20.1,<4.0.0",
+    "filelock>=3.20.3,<3.20.4; python_version < '3.11'",
+    "filelock>=4.2.1,<5.0.0; python_version >= '3.11'",
     "dingtalk-stream>=0.24.3,<0.25.0",
     "grandalf>=0.8.0,<1.0.0",
     "spider-client>=0.0.27,<1.0.0",
@@ -100,7 +104,7 @@ dependencies = [
     "assemblyai>=0.33.0,<1.0.0",
     "fastapi-pagination>=0.13.1,<1.0.0",
     "defusedxml>=0.7.1,<1.0.0",
-    "pypdf>=6.19.0,<7.0.0",
+    "pypdf>=6.21.2,<7.0.0",
     "validators>=0.34.0,<1.0.0",
     "networkx>=3.4.2,<4.0.0",
     "json-repair>=0.30.3,<1.0.0",
@@ -284,7 +288,7 @@ langsmith = ["langsmith>=0.3.42,<1.0.0"]
 arize = ["arize-phoenix-otel>=0.6.1"]
 
 # Individual document loaders
-pypdf = ["pypdf>=6.19.0,<7.0.0"]
+pypdf = ["pypdf>=6.21.2,<7.0.0"]
 docx = ["python-docx>=1.1.0"]
 pytube = ["pytube==15.0.0"]
 
@@ -302,7 +306,11 @@ fake-useragent = ["fake-useragent>=2.0.0"]
 # duckduckgo: the runtime dep for the extracted lfx-duckduckgo bundle.
 # Public consumers should install lfx-duckduckgo directly. The compatibility
 # extra remains so older explicit langflow-base[duckduckgo] installs still work.
-duckduckgo = ["ddgs>=9.0.0"]
+duckduckgo = [
+    "ddgs>=9.0.0",
+    "brotli>=1.2.1,<2.0.0; platform_python_implementation == 'CPython'",
+    "brotlicffi>=1.2.1.0,<2.0.0; platform_python_implementation != 'CPython'",
+]
 yfinance = ["yfinance==0.2.50"]
 wolframalpha = ["wolframalpha==5.1.3"]
 
diff --git a/src/backend/tests/unit/api/v1/test_project_replacement_operations.py b/src/backend/tests/unit/api/v1/test_project_replacement_operations.py
index 8be26afdf3..41cc13acb3 100644
--- a/src/backend/tests/unit/api/v1/test_project_replacement_operations.py
+++ b/src/backend/tests/unit/api/v1/test_project_replacement_operations.py
@@ -349,7 +349,8 @@ async def always_deadlock(self, *args, **kwargs):  # noqa: ARG001 - patched sign
         raise _deadlock_error(statement, message)
 
     monkeypatch.setattr(AsyncSession, "flush", always_deadlock)
-    monkeypatch.setattr(projects_module.asyncio, "sleep", AsyncMock())
+    # Keep the short retry delay real: patching asyncio.sleep also changes the
+    # lifespan's background loops and can make the event-loop monitor spin.
 
     # Spy on _replace_project_operation_once so the retry budget is checked
     # against the number of whole-operation attempts, not just flush calls.
diff --git a/src/backend/tests/unit/services/telemetry_writer/test_process_liveness.py b/src/backend/tests/unit/services/telemetry_writer/test_process_liveness.py
new file mode 100644
index 0000000000..079bd0d3ca
--- /dev/null
+++ b/src/backend/tests/unit/services/telemetry_writer/test_process_liveness.py
@@ -0,0 +1,38 @@
+"""Process probes must recognize dead workers without signaling live workers."""
+
+import subprocess
+import sys
+
+import pytest
+from langflow.services.telemetry_writer.service import _pid_alive
+
+
+@pytest.mark.parametrize("pid", [-1, 0])
+def test_pid_alive_rejects_nonpositive_pids(pid: int) -> None:
+    assert _pid_alive(pid) is False
+
+
+def test_pid_alive_recognizes_exited_process() -> None:
+    with subprocess.Popen([sys.executable, "-c", "pass"]) as process:  # noqa: S603
+        process.wait(timeout=10)
+        assert _pid_alive(process.pid) is False
+
+
+def test_pid_alive_leaves_running_process_responsive() -> None:
+    # Only probe a disposable child: os.kill(pid, 0) terminates Windows
+    # processes instead of performing the harmless existence check on POSIX.
+    with subprocess.Popen(  # noqa: S603
+        [sys.executable, "-c", "import sys; print(sys.stdin.readline().strip(), flush=True)"],
+        stdin=subprocess.PIPE,
+        stdout=subprocess.PIPE,
+        text=True,
+    ) as process:
+        try:
+            assert _pid_alive(process.pid) is True
+            stdout, _ = process.communicate("still-alive\n", timeout=10)
+            assert process.returncode == 0
+            assert stdout.strip() == "still-alive"
+        finally:
+            if process.poll() is None:
+                process.kill()
+                process.wait(timeout=10)
diff --git a/src/backend/tests/unit/services/triggers/test_listener_supervisor.py b/src/backend/tests/unit/services/triggers/test_listener_supervisor.py
index 73e579bb8b..d4fea3da48 100644
--- a/src/backend/tests/unit/services/triggers/test_listener_supervisor.py
+++ b/src/backend/tests/unit/services/triggers/test_listener_supervisor.py
@@ -243,8 +243,11 @@ async def test_persistent_failures_back_off_and_surface_the_error_then_a_success
     from langflow.services.deps import get_settings_service
 
     settings = get_settings_service().settings
-    monkeypatch.setattr(settings, "listener_backoff_base_s", 0.01)
-    monkeypatch.setattr(settings, "listener_backoff_cap_s", 0.02)
+    # Retries below are deliberately forced via _spawn without waiting for
+    # the backoff timer. Use a realistic window so async error persistence and
+    # runner contention cannot consume the entire 20 ms window before assert.
+    monkeypatch.setattr(settings, "listener_backoff_base_s", 5.0)
+    monkeypatch.setattr(settings, "listener_backoff_cap_s", 10.0)
 
     adapter = adapter_registry(RecordingAdapter(fail_with=RuntimeError("provider said no")))
     connection_id = await make_connection()
@@ -263,7 +266,9 @@ async def test_persistent_failures_back_off_and_surface_the_error_then_a_success
     await asyncio.gather(worker.task, return_exceptions=True)
 
     assert worker.consecutive_failures >= 5
+    assert worker.next_attempt_at is not None, "failure must schedule a retry"
     assert worker.resting() is True, "a failed connection must rest before the next attempt"
+    assert worker.resting(now=worker.next_attempt_at) is False, "the retry may resume when backoff expires"
     _state_value, last_error = await _state(trigger_id)
     assert "could not hold this connection" in (last_error or "")
     # The trigger stays armed: a flapping provider is not an owner misconfiguration.
diff --git a/src/backend/tests/unit/services/triggers/test_source_runtime_regressions.py b/src/backend/tests/unit/services/triggers/test_source_runtime_regressions.py
index c3f55956c6..f53521c09b 100644
--- a/src/backend/tests/unit/services/triggers/test_source_runtime_regressions.py
+++ b/src/backend/tests/unit/services/triggers/test_source_runtime_regressions.py
@@ -255,14 +255,44 @@ async def test_gmail_cleanup_preserves_sibling_watch_even_after_opt_out(
 
 @pytest.mark.usefixtures("client")
 async def test_heartbeat_keeps_long_work_owned():
+    """Verify a committed lease extension instead of racing a 900 ms SQLite TTL."""
     name = f"long-work-{uuid4()}"
+    ttl_s = 15.0
     async with session_scope() as session:
-        assert await leases.acquire(session, name=name, owner="first", ttl_s=0.9)
-    task = asyncio.create_task(run_guarded(asyncio.sleep(2), name=name, owner="first", ttl_s=0.9))
-    await asyncio.sleep(1.3)
-    async with session_scope() as session:
-        assert not await leases.acquire(session, name=name, owner="second", ttl_s=10)
-    await task
+        assert await leases.acquire(session, name=name, owner="first", ttl_s=ttl_s)
+        initial = (await session.exec(select(TriggerLease).where(TriggerLease.name == name))).one()
+        original_expiry = initial.expires_at
+
+    renewed = asyncio.Event()
+    finish = asyncio.Event()
+
+    async def on_heartbeat(_session, *, now, ttl_s):  # noqa: ARG001
+        renewed.set()
+
+    task = asyncio.create_task(
+        run_guarded(finish.wait(), name=name, owner="first", ttl_s=ttl_s, heartbeat=on_heartbeat)
+    )
+    try:
+        # The real renewal must complete, including the SQLite commit, before
+        # a competing owner is checked. A generous TTL avoids event-loop/DB
+        # contention turning a valid heartbeat into a spurious lease loss.
+        await asyncio.wait_for(renewed.wait(), timeout=25)
+        for _ in range(50):
+            async with session_scope() as session:
+                lease = (await session.exec(select(TriggerLease).where(TriggerLease.name == name))).one()
+                if lease.expires_at > original_expiry:
+                    assert lease.owner == "first"
+                    assert not await leases.acquire(session, name=name, owner="second", ttl_s=ttl_s)
+                    break
+            await asyncio.sleep(0.1)
+        else:
+            pytest.fail("heartbeat did not persist a lease extension")
+    finally:
+        finish.set()
+        if not task.done():
+            await asyncio.wait_for(task, timeout=10)
+        else:
+            await task
 
 
 @pytest.mark.usefixtures("client")
diff --git a/src/backend/tests/unit/test_docker_security_defaults.py b/src/backend/tests/unit/test_docker_security_defaults.py
index 3cd7a3fe0c..cc59d0decf 100644
--- a/src/backend/tests/unit/test_docker_security_defaults.py
+++ b/src/backend/tests/unit/test_docker_security_defaults.py
@@ -219,8 +219,9 @@ def test_full_target_checks_only_default_workspace_distributions() -> None:
 
 def test_published_images_pin_hardened_package_managers() -> None:
     install_script = (REPO_ROOT / "docker" / "install_hardened_npm.sh").read_text(encoding="utf-8")
+    pip_vendor_script = (REPO_ROOT / "docker" / "patch_hardened_pip_vendor.sh").read_text(encoding="utf-8")
 
-    assert 'NPM_VERSION="12.0.2"' in install_script
+    assert 'NPM_VERSION="12.2.1"' in install_script
     assert 'npm install --global "npm@${NPM_VERSION}"' in install_script
     assert 'actual_npm_version="$(npm --version)"' in install_script
     assert 'if [ "$actual_npm_version" != "$NPM_VERSION" ]; then' in install_script
@@ -229,13 +230,19 @@ def test_published_images_pin_hardened_package_managers() -> None:
         ("IP_ADDRESS_VERSION", "10.7.1", "ip-address", '"ip-address": "10.7.1"'),
         ("BRACE_EXPANSION_VERSION", "5.0.12", "brace-expansion", '"brace-expansion": "5.0.12"'),
         ("TAR_VERSION", "7.5.22", "tar", 'tar: "7.5.22"'),
-        ("UNDICI_VERSION", "6.28.1", "undici", 'undici: "6.28.1"'),
+        ("UNDICI_VERSION", "6.29.1", "undici", 'undici: "6.29.1"'),
+        (
+            "HTTP_CACHE_SEMANTICS_VERSION",
+            "4.3.0",
+            "http-cache-semantics",
+            '"http-cache-semantics": "4.3.0"',
+        ),
     ):
         assert f'{variable}="{version}"' in install_script
         assert f'"{package}@${{{variable}}}"' in install_script
         assert validation_entry in install_script
 
-    assert "for package in ip-address brace-expansion tar undici; do" in install_script
+    assert "for package in ip-address brace-expansion tar undici http-cache-semantics; do" in install_script
     for validation_entry in (
         'sigstore: "5.0.0"',
         '"@sigstore/core": "4.0.1"',
@@ -244,6 +251,34 @@ def test_published_images_pin_hardened_package_managers() -> None:
         assert validation_entry in install_script
     assert "npm ls --global --all --omit=dev" in install_script
     assert 'rm -rf "$npm_cache" /tmp/node-compile-cache' in install_script
+    for package, version in (
+        ("certifi", "2026.10.23"),
+        ("msgpack", "1.2.3"),
+        ("setuptools", "78.1.1"),
+        ("urllib3", "2.8.0"),
+    ):
+        assert f"{package}=={version} --hash=sha256:" in pip_vendor_script
+    assert "--require-hashes" in pip_vendor_script
+    assert "msgpack_hash" in pip_vendor_script
+    assert (
+        "e9b54de76e530bb6cf0ccd036787103acb58fd42ba930ea8bf948ddbaa2f61d1"  # pragma: allowlist secret
+        in pip_vendor_script
+    )
+    assert (
+        "c3a9c4211ff4c309edb8b8c4f1cbfa7ae324c4ba9f91ff254e3d305b9fd54561"  # pragma: allowlist secret
+        in pip_vendor_script
+    )
+    assert (
+        "0cf3cae568d36aa9576b28dfb35f11328f1cb974ca7647d9475ebb86c75ac6e3"  # pragma: allowlist secret
+        in pip_vendor_script
+    )
+    assert 'files("pip._vendor.certifi")' in pip_vendor_script
+    assert 'get_path("pip._vendor.certifi",' in pip_vendor_script
+    assert 'read_text("pip._vendor.certifi",' in pip_vendor_script
+    assert "unexpected urllib3 brotli vendoring block" in pip_vendor_script
+    assert '"br" not in urllib3.util.request.ACCEPT_ENCODING' in pip_vendor_script
+    assert 'shutil.ignore_patterns("tests", "api_tests.txt")' in pip_vendor_script
+    assert 'assert str(pkg_resources.Requirement.parse("openxflow>=1")) == "openxflow>=1"' in pip_vendor_script
 
     for dockerfile in (
         "build_and_push.Dockerfile",
@@ -252,6 +287,8 @@ def test_published_images_pin_hardened_package_managers() -> None:
     ):
         source = (REPO_ROOT / "docker" / dockerfile).read_text(encoding="utf-8")
         assert 'python3.14 -m pip install --no-cache-dir --upgrade "pip==26.2.1"' in source
+        assert "COPY ./docker/patch_hardened_pip_vendor.sh /tmp/patch_hardened_pip_vendor.sh" in source
+        assert "sh /tmp/patch_hardened_pip_vendor.sh" in source
         assert "sh /tmp/install_hardened_npm.sh" in source
 
     backend_project = tomllib.loads(
diff --git a/src/backend/tests/unit/test_memory_base_task.py b/src/backend/tests/unit/test_memory_base_task.py
index 8da42e37f4..2b92fe637f 100644
--- a/src/backend/tests/unit/test_memory_base_task.py
+++ b/src/backend/tests/unit/test_memory_base_task.py
@@ -1550,14 +1550,28 @@ async def _recording_fetch(db, *, flow_id, session_id, cursor_id):  # noqa: ARG0
 
     @pytest.mark.asyncio
     async def test_lock_released_on_task_exception(self, tmp_path):
-        """Lock must be released via finally even when the task raises inside the lock body."""
+        """An exception inside the SQLite fallback lock must release it."""
         import langflow.services.memory_base.task as task_module
 
         memory_base_id = uuid.uuid4()
+        lock_key = (memory_base_id, "s1")
+        task_module._session_ingestion_locks.pop(lock_key, None)
+        # Hold a strong reference because the lock registry uses weak references.
+        lock = task_module._get_or_create_session_lock(lock_key)
 
-        task_module._session_ingestion_locks.pop((memory_base_id, "s1"), None)
+        class InjectedLockError(RuntimeError):
+            """An injected failure raised only after acquiring the session lock."""
+
+        async def fail_while_locked(*_args, **_kwargs):
+            assert lock.locked(), "The exception must occur inside the acquired lock"
+            raise InjectedLockError
 
+        # This test covers the asyncio fallback lock, not database process
+        # ownership. A real shared SQLite checkout races other pytest-xdist
+        # workers and can fail before the injected exception is even reached.
         with (
+            patch("langflow.services.memory_base.task.session_scope", _fake_scope(AsyncMock())),
+            patch("langflow.services.memory_base.task._is_postgres", AsyncMock(return_value=False)),
             patch(
                 "langflow.services.memory_base.task.resolve_local_store_path",
                 return_value=tmp_path,
@@ -1568,9 +1582,9 @@ async def test_lock_released_on_task_exception(self, tmp_path):
             ),
             patch(
                 "langflow.services.memory_base.task._fetch_pending_messages",
-                AsyncMock(side_effect=RuntimeError("DB exploded inside lock")),
-            ),
-            pytest.raises(RuntimeError, match="DB exploded inside lock"),
+                AsyncMock(side_effect=fail_while_locked),
+            ) as pending,
+            pytest.raises(InjectedLockError),
         ):
             await task_module.ingest_memory_task(
                 request=task_module.IngestionRequest(
@@ -1584,7 +1598,7 @@ async def test_lock_released_on_task_exception(self, tmp_path):
                 ),
             )
 
-        lock = task_module._session_ingestion_locks[(memory_base_id, "s1")]
+        pending.assert_awaited_once()
         assert not lock.locked(), "Lock must be released after exception (finally block must have run)"
 
     @pytest.mark.asyncio
diff --git a/src/backend/tests/unit/test_security_dependency_floors.py b/src/backend/tests/unit/test_security_dependency_floors.py
index b7e602701d..62011a1154 100644
--- a/src/backend/tests/unit/test_security_dependency_floors.py
+++ b/src/backend/tests/unit/test_security_dependency_floors.py
@@ -29,6 +29,10 @@ def _requirement(requirements: list[str], name: str) -> Requirement:
     return matches[0]
 
 
+def _requirements(requirements: list[str], name: str) -> list[Requirement]:
+    return [Requirement(spec) for spec in requirements if Requirement(spec).name.lower() == name.lower()]
+
+
 def _assert_specifier(requirement: Requirement, operator: str, version: str) -> None:
     assert any(spec.operator == operator and spec.version == version for spec in requirement.specifier), requirement
 
@@ -47,12 +51,40 @@ def test_workspace_security_overrides_enforce_patched_versions() -> None:
     _assert_floor(gitpython, "3.1.58")
 
     pypdf = _requirement(overrides, "pypdf")
-    _assert_floor(pypdf, "6.19.0")
+    _assert_floor(pypdf, "6.21.2")
     _assert_specifier(pypdf, "<", "7.0.0")
 
     h2 = _requirement(overrides, "h2")
     _assert_floor(h2, "4.4.1")
 
+    black = _requirement(overrides, "black")
+    _assert_floor(black, "26.10.1")
+
+    generator = _requirement(overrides, "datamodel-code-generator")
+    _assert_floor(generator, "0.81.0")
+
+    msgpack = _requirement(overrides, "msgpack")
+    _assert_floor(msgpack, "1.2.1")
+
+    certifi = _requirement(overrides, "certifi")
+    _assert_floor(certifi, "2026.10.19")
+
+    for name, minimum in (("pypdf", "6.21.2"), ("setuptools", "83.0.0")):
+        _assert_floor(_requirement(overrides, name), minimum)
+
+    filelock_requirements = _requirements(overrides, "filelock")
+    assert len(filelock_requirements) == 2
+    python_310 = next(
+        requirement for requirement in filelock_requirements if requirement.marker.evaluate({"python_version": "3.10"})
+    )
+    python_311_plus = next(
+        requirement for requirement in filelock_requirements if requirement.marker.evaluate({"python_version": "3.11"})
+    )
+    _assert_floor(python_310, "3.20.3")
+    _assert_specifier(python_310, "<", "3.20.4")
+    _assert_floor(python_311_plus, "4.2.1")
+    _assert_specifier(python_311_plus, "<", "5.0.0")
+
 
 def test_workspace_security_constraints_enforce_patched_versions() -> None:
     uv_settings = _load_pyproject("pyproject.toml")["tool"]["uv"]
@@ -96,15 +128,41 @@ def test_published_packages_enforce_patched_pypdf_floor() -> None:
     for relative_path in ("src/backend/base/pyproject.toml", "src/lfx/pyproject.toml"):
         dependencies = _load_pyproject(relative_path)["project"]["dependencies"]
         pypdf = _requirement(dependencies, "pypdf")
-        _assert_floor(pypdf, "6.19.0")
+        _assert_floor(pypdf, "6.21.2")
         _assert_specifier(pypdf, "<", "7.0.0")
 
     base_extras = _load_pyproject("src/backend/base/pyproject.toml")["project"]["optional-dependencies"]
     pypdf_extra = _requirement(base_extras["pypdf"], "pypdf")
-    _assert_floor(pypdf_extra, "6.19.0")
+    _assert_floor(pypdf_extra, "6.21.2")
     _assert_specifier(pypdf_extra, "<", "7.0.0")
 
 
+def test_filelock_security_markers_cover_every_supported_python() -> None:
+    for path in ("pyproject.toml", "src/backend/base/pyproject.toml", "src/lfx/pyproject.toml"):
+        project = _load_pyproject(path)
+        requirements = (
+            project["tool"]["uv"]["override-dependencies"]
+            if path == "pyproject.toml"
+            else project["project"]["dependencies"]
+        )
+        for minor in range(10, 15):
+            matches = [
+                requirement
+                for requirement in _requirements(requirements, "filelock")
+                if requirement.marker is None or requirement.marker.evaluate({"python_version": f"3.{minor}"})
+            ]
+            assert len(matches) == 1, (path, minor, matches)
+            requirement = matches[0]
+            if minor == 10:
+                assert "3.20.3" in requirement.specifier
+                assert "3.20.4" not in requirement.specifier
+                assert "4.2.1" not in requirement.specifier
+            else:
+                _assert_floor(requirement, "4.2.1")
+                assert "4.2.4" in requirement.specifier
+                assert "4.2.0" not in requirement.specifier
+
+
 def test_published_packages_enforce_patched_mcp_floor() -> None:
     for relative_path in ("src/backend/base/pyproject.toml", "src/lfx/pyproject.toml"):
         dependencies = _load_pyproject(relative_path)["project"]["dependencies"]
diff --git a/src/bundles/duckduckgo/pyproject.toml b/src/bundles/duckduckgo/pyproject.toml
index 5dfb197abc..84c68d23d6 100644
--- a/src/bundles/duckduckgo/pyproject.toml
+++ b/src/bundles/duckduckgo/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "lfx-duckduckgo"
-version = "0.2.0"
+version = "0.2.1"
 description = "DuckDuckGo Search component as a standalone Langflow Extension Bundle."
 readme = "README.md"
 requires-python = ">=3.10,<3.15"
@@ -21,6 +21,8 @@ dependencies = [
     "lfx>=1.13.0.dev0,<2.0.0",
     "langchain-community>=0.4.1,<1.0.0",
     "ddgs>=9.0.0",
+    "brotli>=1.2.1,<2.0.0; platform_python_implementation == 'CPython'",
+    "brotlicffi>=1.2.1.0,<2.0.0; platform_python_implementation != 'CPython'",
 ]
 
 [project.urls]
diff --git a/src/bundles/duckduckgo/src/lfx_duckduckgo/extension.json b/src/bundles/duckduckgo/src/lfx_duckduckgo/extension.json
index 010c9acfa0..4e2eb16b75 100644
--- a/src/bundles/duckduckgo/src/lfx_duckduckgo/extension.json
+++ b/src/bundles/duckduckgo/src/lfx_duckduckgo/extension.json
@@ -1,7 +1,7 @@
 {
   "$schema": "https://schemas.langflow.org/extension/v1.json",
   "id": "lfx-duckduckgo",
-  "version": "0.2.0",
+  "version": "0.2.1",
   "name": "DuckDuckGo Search",
   "description": "DuckDuckGo Search component as a standalone OpenXFlow Extension Bundle.",
   "lfx": {
diff --git a/src/bundles/nextplaid/pyproject.toml b/src/bundles/nextplaid/pyproject.toml
index cc6ae1088f..c6ae385f4e 100644
--- a/src/bundles/nextplaid/pyproject.toml
+++ b/src/bundles/nextplaid/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "lfx-nextplaid"
-version = "0.2.0"
+version = "0.2.1"
 description = "NextPlaid multi-vector vector store (+ vLLM multivector embeddings) as a standalone Langflow Extension Bundle."
 readme = "README.md"
 requires-python = ">=3.10,<3.15"
@@ -30,7 +30,9 @@ dependencies = [
     "lfx>=1.13.0.dev0,<2.0.0",
     "langchain-plaid>=0.1.0,<1.0.0",
     "httpx>=0.28.1,<1.0.0",
-    "pillow>=12.3.0,<13.0.0",
+    # Pillow 13 ships security fixes but dropped Python 3.10 support.
+    "pillow>=12.3.0,<13.0.0; python_version < '3.11'",
+    "pillow>=13.0.0,<14.0.0; python_version >= '3.11'",
 ]
 
 [project.urls]
diff --git a/src/bundles/nextplaid/src/lfx_nextplaid/extension.json b/src/bundles/nextplaid/src/lfx_nextplaid/extension.json
index 46dc9d617b..93158916ae 100644
--- a/src/bundles/nextplaid/src/lfx_nextplaid/extension.json
+++ b/src/bundles/nextplaid/src/lfx_nextplaid/extension.json
@@ -1,7 +1,7 @@
 {
   "$schema": "https://schemas.langflow.org/extension/v1.json",
   "id": "lfx-nextplaid",
-  "version": "0.2.0",
+  "version": "0.2.1",
   "name": "NextPlaid",
   "description": "NextPlaid multi-vector (ColBERT/PLAID) vector store and its companion vLLM multivector embeddings as a standalone OpenXFlow Extension Bundle.",
   "lfx": {
diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json
index 11bca8f914..0a12648396 100644
--- a/src/frontend/package-lock.json
+++ b/src/frontend/package-lock.json
@@ -48,7 +48,7 @@
         "clsx": "^2.1.1",
         "cmdk": "^1.1.1",
         "date-fns": "^4.4.0",
-        "dompurify": "^3.4.16",
+        "dompurify": "^3.4.17",
         "dotenv": "^16.4.5",
         "elkjs": "^0.9.3",
         "emoji-regex": "^10.3.0",
@@ -99,7 +99,7 @@
         "vanilla-jsoneditor": "^2.4.0",
         "web-vitals": "^4.1.1",
         "whatwg-fetch": "^3.6.20",
-        "zod": "^3.23.8",
+        "zod": "^4.6.7",
         "zustand": "^4.5.2"
       },
       "devDependencies": {
@@ -1487,21 +1487,32 @@
       "license": "MIT"
     },
     "node_modules/@emnapi/core": {
-      "version": "1.10.0",
-      "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.10.0.tgz",
-      "integrity": "sha512-yq6OkJ4p82CAfPl0u9mQebQHKPJkY7WrIuk205cTYnYe+k2Z8YBh11FrbRG/H6ihirqcacOgl2BIO8oyMQLeXw==",
+      "version": "1.11.0",
+      "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.11.0.tgz",
+      "integrity": "sha512-l9Oo58x0HOP5znGzVhYW9U3e5wVuA4LAZU2AGezTmkhO1CgQRFDhDg4nneHsu/t3WniXg9QrG2nIXL/ZS8ln8Q==",
+      "dev": true,
+      "license": "MIT",
+      "optional": true,
+      "dependencies": {
+        "@emnapi/wasi-threads": "1.2.2",
+        "tslib": "^2.4.0"
+      }
+    },
+    "node_modules/@emnapi/core/node_modules/@emnapi/wasi-threads": {
+      "version": "1.2.2",
+      "resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.2.tgz",
+      "integrity": "sha512-c95qOXkHdydNKhscBTebqEC1CVAZpyqOfVfBzQ1qgzyl3gfeldUjIggDbIZgDKsHLgnsM+igH7TJ/eAasaVuMA==",
       "dev": true,
       "license": "MIT",
       "optional": true,
       "dependencies": {
-        "@emnapi/wasi-threads": "1.2.1",
         "tslib": "^2.4.0"
       }
     },
     "node_modules/@emnapi/runtime": {
-      "version": "1.10.0",
-      "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.10.0.tgz",
-      "integrity": "sha512-ewvYlk86xUoGI0zQRNq/mC+16R1QeDlKQy21Ki3oSYXNgLb45GV1P6A0M+/s6nyCuNDqe5VpaY84BzXGwVbwFA==",
+      "version": "1.11.0",
+      "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.0.tgz",
+      "integrity": "sha512-55coeOFKHv1ywEcUXJtWU5f+Jr/W5tZDvZig8DLKSwUN1JpROQ4rk/SNOQiFWmaR/VKF4zuFyW1B8JduOSv6Pg==",
       "dev": true,
       "license": "MIT",
       "optional": true,
@@ -3331,75 +3342,755 @@
       "resolved": "https://registry.npmjs.org/@modelcontextprotocol/server-everything/-/server-everything-2026.7.4.tgz",
       "integrity": "sha512-ydMW/M6rk9tK23b+U38trsNLHhd5eF+ntiv2Vr+RPMDhbiKY/IKrZU25ukvSXVPUBvy7TxTPWpeV4KcYcXg72w==",
       "dev": true,
-      "license": "SEE LICENSE IN LICENSE",
-      "dependencies": {
-        "@modelcontextprotocol/sdk": "^1.29.0",
-        "cors": "^2.8.5",
-        "express": "^5.2.1",
-        "jszip": "^3.10.1",
-        "zod": "^4.0.0"
-      },
-      "bin": {
-        "mcp-server-everything": "dist/index.js"
-      }
+      "license": "SEE LICENSE IN LICENSE",
+      "dependencies": {
+        "@modelcontextprotocol/sdk": "^1.29.0",
+        "cors": "^2.8.5",
+        "express": "^5.2.1",
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+        "@oxc-resolver/binding-linux-s390x-gnu": "11.21.2",
+        "@oxc-resolver/binding-linux-x64-gnu": "11.21.2",
+        "@oxc-resolver/binding-linux-x64-musl": "11.21.2",
+        "@oxc-resolver/binding-openharmony-arm64": "11.21.2",
+        "@oxc-resolver/binding-wasm32-wasi": "11.21.2",
+        "@oxc-resolver/binding-win32-arm64-msvc": "11.21.2",
+        "@oxc-resolver/binding-win32-x64-msvc": "11.21.2"
+      }
+    },
     "node_modules/p-debounce": {
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/p-debounce/-/p-debounce-4.0.0.tgz",
@@ -16222,19 +17162,19 @@
       }
     },
     "node_modules/puppeteer": {
-      "version": "25.1.0",
-      "resolved": "https://registry.npmjs.org/puppeteer/-/puppeteer-25.1.0.tgz",
-      "integrity": "sha512-7L6/0JM7XStK99lIL4xQySyNEXNfII6pk0BxkI5kKBTOhR7AsoQiv067YTsE/rIXxQiq9ajlO4WcqBjS/FWK1A==",
+      "version": "25.16.0",
+      "resolved": "https://registry.npmjs.org/puppeteer/-/puppeteer-25.16.0.tgz",
+      "integrity": "sha512-LOr3reYnxAYYfQkpopz1fQnL0LdvqhpR5U2Fu+QnwzTX2gpVTHsn91UETo5NNC1md2/oC54Zu1ChBXgZDNSP1A==",
       "dev": true,
       "hasInstallScript": true,
       "license": "Apache-2.0",
       "dependencies": {
-        "@puppeteer/browsers": "3.0.4",
-        "chromium-bidi": "16.0.1",
-        "devtools-protocol": "0.0.1624250",
+        "@puppeteer/browsers": "3.2.6",
+        "chromium-bidi": "157.0.8090-0",
+        "devtools-protocol": "0.0.1704951",
         "lilconfig": "^3.1.3",
-        "puppeteer-core": "25.1.0",
-        "typed-query-selector": "^2.12.2"
+        "puppeteer-core": "25.16.0",
+        "typed-query-selector": "^2.12.3"
       },
       "bin": {
         "puppeteer": "lib/puppeteer/node/cli.js"
@@ -16244,18 +17184,18 @@
       }
     },
     "node_modules/puppeteer-core": {
-      "version": "25.1.0",
-      "resolved": "https://registry.npmjs.org/puppeteer-core/-/puppeteer-core-25.1.0.tgz",
-      "integrity": "sha512-jKzy5y4WG6uNuFbTWgW1D7mqoT9o0nllc/6a1DGF775T1mPmgw3scdFEtEq67yVFikavQmbYq6NLfbTfxHSlqQ==",
+      "version": "25.16.0",
+      "resolved": "https://registry.npmjs.org/puppeteer-core/-/puppeteer-core-25.16.0.tgz",
+      "integrity": "sha512-VA5Pf54N8shfopNkZZXCdiiwfnnY8ZJYAMqFQRhlH5mGUhfwf53Ar770GPtdpTDLUqvQeYpIBtC9/G2Jij2IJw==",
       "dev": true,
       "license": "Apache-2.0",
       "dependencies": {
-        "@puppeteer/browsers": "3.0.4",
-        "chromium-bidi": "16.0.1",
-        "devtools-protocol": "0.0.1624250",
-        "typed-query-selector": "^2.12.2",
-        "webdriver-bidi-protocol": "0.4.2",
-        "ws": "^8.21.0"
+        "@puppeteer/browsers": "3.2.6",
+        "chromium-bidi": "157.0.8090-0",
+        "devtools-protocol": "0.0.1704951",
+        "typed-query-selector": "^2.12.3",
+        "webdriver-bidi-protocol": "1.0.1",
+        "ws": "^8.22.0"
       },
       "engines": {
         "node": ">=22.12.0"
@@ -16279,9 +17219,9 @@
       "license": "MIT"
     },
     "node_modules/qs": {
-      "version": "6.16.0",
-      "resolved": "https://registry.npmjs.org/qs/-/qs-6.16.0.tgz",
-      "integrity": "sha512-h6fhOIaRrID2CbEY2fqs+7t+UXZo+MLAnU5gRIq85uFtdiUPCdsApMlHhXogKVM4HM2DVbIjGNTTYH2OcmP1vA==",
+      "version": "6.16.1",
+      "resolved": "https://registry.npmjs.org/qs/-/qs-6.16.1.tgz",
+      "integrity": "sha512-5Px6J7rfHBAGUhNQ3es4YzcOdBBw0SDcVtThJml4RdnKw4tKs2epA0kPZ+K8X+WiHktT93RHVcQ/3ym5aTCgtg==",
       "dev": true,
       "license": "BSD-3-Clause",
       "dependencies": {
@@ -17892,25 +18832,29 @@
       }
     },
     "node_modules/storybook": {
-      "version": "10.3.6",
-      "resolved": "https://registry.npmjs.org/storybook/-/storybook-10.3.6.tgz",
-      "integrity": "sha512-vbSz7g/1rGMC1uAULqMZjALkIuLu2QABqfhRYhyr/11kzyesi+vAmwyJLukZP1FfecxGOgMwOh6GS0YsGpHAvQ==",
+      "version": "10.6.3",
+      "resolved": "https://registry.npmjs.org/storybook/-/storybook-10.6.3.tgz",
+      "integrity": "sha512-lNBvcZFG44C98KvQHYzfq1O46m+sl94zY1+na5/9E3ihtNMbvpOgYyMFMIcWYUvOMo3OM4q9jzvB73KU20bU8w==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@storybook/global": "^5.0.0",
-        "@storybook/icons": "^2.0.1",
-        "@testing-library/jest-dom": "^6.9.1",
-        "@testing-library/user-event": "^14.6.1",
+        "@storybook/icons": "^2.0.2",
+        "@testing-library/dom": "^10.4.1",
+        "@testing-library/jest-dom": "6.9.1",
+        "@testing-library/user-event": "^14.6.3",
         "@vitest/expect": "3.2.4",
         "@vitest/spy": "3.2.4",
         "@webcontainer/env": "^1.1.1",
-        "esbuild": "^0.18.0 || ^0.19.0 || ^0.20.0 || ^0.21.0 || ^0.22.0 || ^0.23.0 || ^0.24.0 || ^0.25.0 || ^0.26.0 || ^0.27.0",
+        "esbuild": "^0.18.0 || ^0.19.0 || ^0.20.0 || ^0.21.0 || ^0.22.0 || ^0.23.0 || ^0.24.0 || ^0.25.0 || ^0.26.0 || ^0.27.0 || ^0.28.0",
+        "jsonc-parser": "^3.3.1",
         "open": "^10.2.0",
+        "oxc-parser": "^0.127.0",
+        "oxc-resolver": "11.21.2",
         "recast": "^0.23.5",
         "semver": "^7.7.3",
         "use-sync-external-store": "^1.5.0",
-        "ws": "^8.18.0"
+        "ws": "^8.21.1"
       },
       "bin": {
         "storybook": "dist/bin/dispatcher.js"
@@ -17920,10 +18864,14 @@
         "url": "https://opencollective.com/storybook"
       },
       "peerDependencies": {
+        "@types/react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0",
         "prettier": "^2 || ^3",
-        "vite-plus": "^0.1.15"
+        "vite-plus": "^0.1.15 || ^0.2.0"
       },
       "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
         "prettier": {
           "optional": true
         },
@@ -18794,9 +19742,9 @@
       }
     },
     "node_modules/typed-query-selector": {
-      "version": "2.12.2",
-      "resolved": "https://registry.npmjs.org/typed-query-selector/-/typed-query-selector-2.12.2.tgz",
-      "integrity": "sha512-EOPFbyIub4ngnEdqi2yOcNeDLaX/0jcE1JoAXQDDMIthap7FoN795lc/SHfIq2d416VufXpM8z/lD+WRm2gfOQ==",
+      "version": "2.12.3",
+      "resolved": "https://registry.npmjs.org/typed-query-selector/-/typed-query-selector-2.12.3.tgz",
+      "integrity": "sha512-/dtuMZyutHLlrxWhhC1uib00ELUr2dZ8C9B8YzZYsJmeE3V56OxRblrSM2UaF142pcMtak7ehZ6+poWGeehNIg==",
       "dev": true,
       "license": "MIT"
     },
@@ -19305,9 +20253,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "7.3.6",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-7.3.6.tgz",
-      "integrity": "sha512-4XP60spRGjSZFf1qYH+dJIkK2znL3zQfl9KkOV9MkkRR/3Dls0dxaBsQPTloEc5BLXWPL9vsOxopxyKoMmDueg==",
+      "version": "7.3.7",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-7.3.7.tgz",
+      "integrity": "sha512-AHEzTORMbi7/7aXp7w7KZ5+DCUdSeLsJz0grY8xHpc9DHdYb0vQu6DqJfm2tOw3gDExNgPgnJTXxM6syYFHaCA==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -19987,9 +20935,9 @@
       "license": "Apache-2.0"
     },
     "node_modules/webdriver-bidi-protocol": {
-      "version": "0.4.2",
-      "resolved": "https://registry.npmjs.org/webdriver-bidi-protocol/-/webdriver-bidi-protocol-0.4.2.tgz",
-      "integrity": "sha512-VSV+fzfChirL3e7jay2yUC7B4HQCGtEWEg/MSSQbK+qWbqeGlRLlXTzPpYr3XGUvbpDHumWZBJxgesg4N7dbtA==",
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/webdriver-bidi-protocol/-/webdriver-bidi-protocol-1.0.1.tgz",
+      "integrity": "sha512-WXWs/aXF2a6Al1Z2/V/7Gp2oPZOAAmHjJOTmv4KCXRJ122IMj3tSSMc6a4q3K4QOmhQbse/Ahr+oTVJOU2dUiw==",
       "dev": true,
       "license": "Apache-2.0"
     },
@@ -20283,9 +21231,9 @@
       "license": "MIT"
     },
     "node_modules/zod": {
-      "version": "3.25.76",
-      "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz",
-      "integrity": "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==",
+      "version": "4.6.7",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.6.7.tgz",
+      "integrity": "sha512-g8W2D2kU6a8untc053fX1uVBKzM7yqAxW/3zwk83+6RyF2+SKOuV+c0wBDhUwzV1PaGapmZH+rmcUzPf+3CGwg==",
       "license": "MIT",
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
diff --git a/src/frontend/package.json b/src/frontend/package.json
index 53248058be..d383230ea8 100644
--- a/src/frontend/package.json
+++ b/src/frontend/package.json
@@ -30,7 +30,8 @@
       "js-yaml": "^4.3.1"
     },
     "accessibility-checker": {
-      "js-yaml": "^4.3.1"
+      "js-yaml": "^4.3.1",
+      "puppeteer": "^25.12.1"
     },
     "speech-rule-engine": {
       "@xmldom/xmldom": "^0.9.12"
@@ -49,7 +50,9 @@
     "undici": "^8.10.2",
     "postcss-selector-parser": "^7.1.6",
     "form-data": "^4.0.6",
-    "fast-uri": "^4.1.5",
+    "fast-uri": "^4.2.3",
+    "follow-redirects": "^1.16.1",
+    "zod": "^4.6.7",
     "proxy-addr": "^2.0.8",
     "adm-zip": "^0.6.0",
     "ws": "^8.21.3",
@@ -103,7 +106,7 @@
     "clsx": "^2.1.1",
     "cmdk": "^1.1.1",
     "date-fns": "^4.4.0",
-    "dompurify": "^3.4.16",
+    "dompurify": "^3.4.17",
     "dotenv": "^16.4.5",
     "elkjs": "^0.9.3",
     "emoji-regex": "^10.3.0",
@@ -154,7 +157,7 @@
     "vanilla-jsoneditor": "^2.4.0",
     "web-vitals": "^4.1.1",
     "whatwg-fetch": "^3.6.20",
-    "zod": "^3.23.8",
+    "zod": "^4.6.7",
     "zustand": "^4.5.2"
   },
   "scripts": {
@@ -168,7 +171,7 @@
     "a11y:html-report": "node tests/utils/build-a11y-html-report.mjs",
     "a11y:job-summary": "node tests/utils/build-a11y-job-summary.mjs",
     "e2e:check-report": "node tests/utils/check-playwright-report.mjs",
-    "test:e2e-utilities": "node --test tests/utils/blocking-provider-policy.test.mjs tests/utils/check-playwright-report.test.mjs tests/utils/flow-editor-persistence-policy.test.mjs tests/utils/get-default-project-id-for-test.test.mjs tests/utils/loopback-provider-policy.test.mjs tests/utils/server-error-contract.test.mjs",
+    "test:e2e-utilities": "node --test tests/utils/blocking-provider-policy.test.mjs tests/utils/check-playwright-report.test.mjs tests/utils/chromium-bidi-zod-compatibility.test.mjs tests/utils/flow-editor-persistence-policy.test.mjs tests/utils/get-default-project-id-for-test.test.mjs tests/utils/loopback-provider-policy.test.mjs tests/utils/server-error-contract.test.mjs",
     "serve": "vite preview",
     "format": "npx @biomejs/biome format --write",
     "lint": "npx @biomejs/biome lint",
diff --git a/src/frontend/tests/utils/chromium-bidi-zod-compatibility.test.mjs b/src/frontend/tests/utils/chromium-bidi-zod-compatibility.test.mjs
new file mode 100644
index 0000000000..05950ad4c5
--- /dev/null
+++ b/src/frontend/tests/utils/chromium-bidi-zod-compatibility.test.mjs
@@ -0,0 +1,22 @@
+import assert from "node:assert/strict";
+import { createRequire } from "node:module";
+import path from "node:path";
+import { test } from "node:test";
+import { pathToFileURL } from "node:url";
+
+const require = createRequire(import.meta.url);
+const parserRoot = path.dirname(require.resolve("chromium-bidi"));
+const { Browser } = await import(
+  pathToFileURL(path.join(parserRoot, "protocol-parser", "protocol-parser.js"))
+    .href
+);
+
+test("Chromium BiDi converts invalid parameters into protocol errors", () => {
+  assert.throws(
+    () =>
+      Browser.parseCreateUserContextParameters({
+        acceptInsecureCerts: "invalid",
+      }),
+    (error) => error.constructor.name === "InvalidArgumentException",
+  );
+});
diff --git a/src/lfx/pyproject.toml b/src/lfx/pyproject.toml
index ebcbe5365f..90249a9321 100644
--- a/src/lfx/pyproject.toml
+++ b/src/lfx/pyproject.toml
@@ -16,7 +16,9 @@ dependencies = [
     # xlsx path survives torch/docling becoming opt-in.
     "openpyxl>=3.1.5,<4.0.0",
     "pydantic>=2.0.0,<2.14",
-    "pillow>=12.3.0,<13.0.0",
+    # Pillow 13 ships security fixes but dropped Python 3.10 support.
+    "pillow>=12.3.0,<13.0.0; python_version < '3.11'",
+    "pillow>=13.0.0,<14.0.0; python_version >= '3.11'",
     "fastapi>=0.135.0,<1.0.0",
     # [standard] adds uvloop + httptools (fast loop + HTTP parser) for the `lfx serve` worker path.
     "uvicorn[standard]>=0.34.3,<1.0.0",
@@ -55,8 +57,9 @@ dependencies = [
     "langchain-classic~=1.0.7",
     "sqlalchemy>=2.0.38,<2.1.0",
     "validators>=0.34.0,<1.0.0",
-    "filelock>=3.20.1,<4.0.0",
-    "pypdf>=6.19.0,<7.0.0",
+    "filelock>=3.20.3,<3.20.4; python_version < '3.11'",
+    "filelock>=4.2.1,<5.0.0; python_version >= '3.11'",
+    "pypdf>=6.21.2,<7.0.0",
     "cryptography>=50.0.0",
     "pyjwt>=2.15.1,<3.0.0",  # CVE-2026-102266, CVE-2026-102267, CVE-2026-102271, CVE-2026-102272, CVE-2026-102274
     "ag-ui-protocol>=0.1.10",
diff --git a/src/lfx/src/lfx/_assets/component_index.json b/src/lfx/src/lfx/_assets/component_index.json
index 2a79128e25..8e89ad1e04 100644
--- a/src/lfx/src/lfx/_assets/component_index.json
+++ b/src/lfx/src/lfx/_assets/component_index.json
@@ -4448,7 +4448,7 @@
           "icon": "layout-template",
           "legacy": false,
           "metadata": {
-            "code_hash": "a6429c7f9670",
+            "code_hash": "8052088025a1",
             "component_class_name": "URLComponent",
             "dependencies": {
               "dependencies": [
@@ -4460,13 +4460,13 @@
                   "name": "bs4",
                   "version": "4.15.0"
                 },
-                {
-                  "name": "markitdown",
-                  "version": "0.1.6"
-                },
                 {
                   "name": "lfx",
                   "version": null
+                },
+                {
+                  "name": "markitdown",
+                  "version": "0.1.6"
                 }
               ],
               "total_dependencies": 4
@@ -4567,7 +4567,7 @@
               "show": true,
               "title_case": false,
               "type": "code",
-              "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\nfrom markitdown import MarkItDown\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
+              "value": "import importlib.util\nimport io\nimport os\nimport re\nimport zlib\nfrom urllib.parse import urljoin, urlparse\n\nimport httpx\nfrom bs4 import BeautifulSoup\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.helpers.data import safe_convert\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service\nfrom lfx.utils.request_utils import get_user_agent\nfrom lfx.utils.ssrf_protection import SSRFProtectionError, is_ssrf_protection_enabled, validate_and_resolve_url\nfrom lfx.utils.ssrf_transport import create_ssrf_protected_client, pin_host_for_url\n\n# Constants\nDEFAULT_TIMEOUT = 30\nDEFAULT_MAX_DEPTH = 1\nDEFAULT_FORMAT = \"Text\"\n\n# HTTP status codes that carry a redirect Location header (RFC 9110).\nREDIRECT_STATUS_CODES = frozenset({301, 302, 303, 307, 308})\n\n# Maximum number of redirects to follow. Each hop is re-validated for SSRF safety;\n# matches httpx's default and the API Request component.\nMAX_REDIRECTS = 20\n\n# Default ports per scheme, used to compare redirect origins.\nDEFAULT_SCHEME_PORTS = {\"http\": 80, \"https\": 443}\n\n# Memory bounds for untrusted responses. Bodies are streamed and abandoned once the\n# per-response cap or the budget shared by every page of one fetch (all URLs and crawled\n# links) is exceeded, so a hostile server cannot exhaust memory with a huge or endless body.\n# Configurable via LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES / LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\n# (see RuntimeSettings); these are only the fallback used when the settings service isn't available.\nFALLBACK_MAX_RESPONSE_BYTES = 10 * 1024 * 1024\nFALLBACK_MAX_TOTAL_BYTES = 100 * 1024 * 1024\n\n# Only advertise codings we can decode with a strict output limit. Raw response bytes\n# are also capped before decoding, including compressed headers and trailing data.\nACCEPT_ENCODING = \"gzip, deflate\"\nALLOWED_CONTENT_ENCODINGS = frozenset({\"gzip\", \"deflate\"})\nZLIB_HEADER_BYTES = 2\n\n\nURL_REGEX = re.compile(\n    r\"^(https?:\\/\\/)?\" r\"(www\\.)?\" r\"([a-zA-Z0-9.-]+)\" r\"(\\.[a-zA-Z]{2,})?\" r\"(:\\d+)?\" r\"(\\/[^\\s]*)?$\",\n    re.IGNORECASE,\n)\n\nUSER_AGENT = None\n# Check if langflow is installed using importlib.util.find_spec(name))\nif importlib.util.find_spec(\"langflow\"):\n    langflow_installed = True\n    USER_AGENT = get_user_agent()\nelse:\n    langflow_installed = False\n    USER_AGENT = \"lfx\"\n\n\nclass URLComponent(Component):\n    \"\"\"A component that loads and parses content from web pages recursively.\n\n    This component allows fetching content from one or more URLs, with options to:\n    - Control crawl depth\n    - Prevent crawling outside the root domain\n    - Use async loading for better performance\n    - Extract either raw HTML or clean text\n    - Configure request headers and timeouts\n    \"\"\"\n\n    display_name = \"URL\"\n    description = \"Fetch content from one or more web pages, following links recursively.\"\n    documentation: str = \"https://docs.langflow.org/url\"\n    icon = \"layout-template\"\n    name = \"URLComponent\"\n    # Body bytes the current fetch may still read; reset by ``fetch_url_contents``.\n    _bytes_remaining = FALLBACK_MAX_TOTAL_BYTES\n    # Per-response and total byte caps for the current fetch; resolved from settings by\n    # ``fetch_url_contents``. Kept as fallbacks here so the class works if that never runs.\n    _max_response_bytes = FALLBACK_MAX_RESPONSE_BYTES\n    _max_total_bytes = FALLBACK_MAX_TOTAL_BYTES\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs to crawl recursively, by clicking the '+' button.\",\n            is_list=True,\n            tool_mode=True,\n            placeholder=\"Enter a URL...\",\n            list_add_label=\"Add URL\",\n            input_types=[\"Message\"],\n        ),\n        SliderInput(\n            name=\"max_depth\",\n            display_name=\"Depth\",\n            info=(\n                \"Controls how many 'clicks' away from the initial page the crawler will go:\\n\"\n                \"- depth 1: only the initial page\\n\"\n                \"- depth 2: initial page + all pages linked directly from it\\n\"\n                \"- depth 3: initial page + direct links + links found on those direct link pages\\n\"\n                \"Note: This is about link traversal, not URL path depth.\"\n            ),\n            value=DEFAULT_MAX_DEPTH,\n            range_spec=RangeSpec(min=1, max=5, step=1),\n            required=False,\n            min_label=\" \",\n            max_label=\" \",\n            min_label_icon=\"None\",\n            max_label_icon=\"None\",\n            # slider_input=True\n        ),\n        BoolInput(\n            name=\"prevent_outside\",\n            display_name=\"Prevent Outside\",\n            info=(\n                \"If enabled, only crawls URLs within the same domain as the root URL. \"\n                \"This helps prevent the crawler from going to external websites.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"use_async\",\n            display_name=\"Use Async\",\n            info=(\n                \"If enabled, uses asynchronous loading which can be significantly faster \"\n                \"but might use more system resources.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"follow_redirects\",\n            display_name=\"Follow Redirects\",\n            info=(\n                \"If enabled, follows HTTP redirects such as http→https or www/non-www \"\n                \"normalization, which most sites rely on to serve their content. When SSRF \"\n                \"protection is enabled (the default), every redirect hop is re-validated against \"\n                \"the same blocked-IP denylist and DNS-pinned before it is fetched, so following \"\n                \"redirects cannot be used to reach internal resources. Disable to capture the \"\n                \"first response as-is without following redirects.\"\n            ),\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"format\",\n            display_name=\"Output Format\",\n            info=(\n                \"Output Format. Use 'Text' to extract the text from the HTML, \"\n                \"'Markdown' to parse the HTML into Markdown format, or 'HTML' \"\n                \"for the raw HTML content.\"\n            ),\n            options=[\"Text\", \"HTML\", \"Markdown\"],\n            value=DEFAULT_FORMAT,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"timeout\",\n            display_name=\"Timeout\",\n            info=\"Timeout for the request in seconds.\",\n            value=DEFAULT_TIMEOUT,\n            required=False,\n            advanced=True,\n        ),\n        TableInput(\n            name=\"headers\",\n            display_name=\"Headers\",\n            info=\"The headers to send with the request\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Header\",\n                    \"type\": \"str\",\n                    \"description\": \"Header name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Header value\",\n                },\n            ],\n            value=[{\"key\": \"User-Agent\", \"value\": USER_AGENT}],\n            advanced=True,\n            input_types=[\"DataFrame\", \"Table\"],\n        ),\n        BoolInput(\n            name=\"filter_text_html\",\n            display_name=\"Filter Text/HTML\",\n            info=\"If enabled, filters out text/css content type from the results.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"continue_on_failure\",\n            display_name=\"Continue on Failure\",\n            info=\"If enabled, continues crawling even if some requests fail.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"check_response_status\",\n            display_name=\"Check Response Status\",\n            info=\"If enabled, checks the response status of the request.\",\n            value=False,\n            required=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autoset_encoding\",\n            display_name=\"Autoset Encoding\",\n            info=\"If enabled, automatically sets the encoding of the request.\",\n            value=True,\n            required=False,\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Extracted Pages\", name=\"page_results\", method=\"fetch_content\"),\n        Output(display_name=\"Raw Content\", name=\"raw_results\", method=\"fetch_content_as_message\", tool_mode=False),\n    ]\n\n    @staticmethod\n    def _html_extractor(x: str) -> str:\n        \"\"\"Extract raw HTML content.\"\"\"\n        return x\n\n    @staticmethod\n    def _text_extractor(x: str) -> str:\n        \"\"\"Extract clean text from HTML.\"\"\"\n        return BeautifulSoup(x, \"lxml\").get_text()\n\n    @staticmethod\n    def _markdown_extractor(x: str) -> str:\n        \"\"\"Convert HTML to Markdown format.\"\"\"\n        # markitdown imports magika, which loads the nearest .env at import time. Keep that\n        # optional converter out of component discovery and load it only when Markdown output\n        # is actually requested.\n        from markitdown import MarkItDown\n\n        stream = io.BytesIO(x.encode(\"utf-8\"))\n        result = MarkItDown(enable_plugins=False).convert_stream(stream)\n        return result.markdown\n\n    @staticmethod\n    def validate_url(url: str) -> bool:\n        \"\"\"Validates if the given string matches URL pattern.\n\n        Args:\n            url: The URL string to validate\n\n        Returns:\n            bool: True if the URL is valid, False otherwise\n        \"\"\"\n        return bool(URL_REGEX.match(url))\n\n    def ensure_url(self, url: str) -> tuple[str, list[str]]:\n        \"\"\"Ensures the given string is a valid URL and returns validated IPs for DNS pinning.\n\n        Args:\n            url: The URL string to validate and normalize\n\n        Returns:\n            tuple[str, list[str]]: The normalized URL and list of validated IPs for DNS pinning\n\n        Raises:\n            ValueError: If the URL is invalid or blocked by SSRF protection\n        \"\"\"\n        url = url.strip()\n        if not url.startswith((\"http://\", \"https://\")):\n            url = \"https://\" + url\n\n        if not self.validate_url(url):\n            msg = f\"Invalid URL: {url}\"\n            raise ValueError(msg)\n\n        # ============================================================================\n        # SSRF Protection with DNS Pinning\n        # ============================================================================\n        # This prevents DNS rebinding attacks by:\n        # 1. Resolving DNS and validating IPs during security check\n        # 2. Returning the validated IP addresses for DNS pinning\n        # 3. Using a custom HTTP transport that forces use of the pinned IPs\n        # 4. Ignoring any new DNS resolutions (prevents rebinding)\n        #\n        # Without DNS pinning, an attacker could:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - Second DNS lookup: returns internal IP (bypasses protection)\n        # - Attack succeeds: accesses internal services\n        #\n        # With DNS pinning:\n        # - First DNS lookup: returns public IP (passes validation)\n        # - IPs are pinned and returned\n        # - HTTP request: uses pinned IPs directly (no new DNS lookup)\n        # - Attack fails: even if DNS changes, we use the validated IPs\n        # ============================================================================\n        try:\n            _validated_url, validated_ips = validate_and_resolve_url(url)\n        except SSRFProtectionError as e:\n            msg = f\"SSRF Protection: {e}\"\n            raise ValueError(msg) from e\n        else:\n            # Log DNS pinning information for security auditing\n            if validated_ips:\n                logger.debug(f\"SSRF Protection: Using DNS pinning with {len(validated_ips)} validated IP(s) for {url}\")\n\n            return url, validated_ips\n\n    def _build_http_client(self, url: str, validated_ips: list[str]) -> httpx.AsyncClient:\n        \"\"\"Create an HTTP client with DNS pinning for SSRF protection.\n\n        Args:\n            url: The request URL whose hostname will be pinned\n            validated_ips: IPs validated by validate_and_resolve_url for this URL\n\n        Returns:\n            httpx.AsyncClient: A client with DNS pinning when SSRF protection is enabled\n        \"\"\"\n        hostname = pin_host_for_url(url) if is_ssrf_protection_enabled() and validated_ips else None\n        if hostname:\n            client = create_ssrf_protected_client(hostname=hostname, validated_ips=validated_ips)\n        else:\n            client = httpx.AsyncClient()\n        # A client default, so a user-supplied Accept-Encoding header still takes precedence.\n        client.headers[\"Accept-Encoding\"] = ACCEPT_ENCODING\n        return client\n\n    @staticmethod\n    def _headers_for_redirect(headers: dict | None, current_url: str, next_url: str) -> dict | None:\n        \"\"\"Drop sensitive headers when a redirect crosses to a different origin.\n\n        Mirrors httpx's auto-follow behavior so manually following redirects does not\n        leak credentials (Authorization / Cookie) to an origin other than the one the\n        caller intended them for. Headers are kept only when the redirect stays on the\n        same origin (scheme, host, port) or is a direct https upgrade of the same host\n        on default ports - the exact cases where httpx keeps the Authorization header.\n        \"\"\"\n        if not headers:\n            return headers\n        current, nxt = urlparse(current_url), urlparse(next_url)\n        current_port = current.port or DEFAULT_SCHEME_PORTS.get(current.scheme)\n        next_port = nxt.port or DEFAULT_SCHEME_PORTS.get(nxt.scheme)\n        same_origin = (current.scheme, current.hostname, current_port) == (nxt.scheme, nxt.hostname, next_port)\n        https_upgrade = (\n            current.hostname == nxt.hostname\n            and current.scheme == \"http\"\n            and nxt.scheme == \"https\"\n            and current_port == DEFAULT_SCHEME_PORTS[\"http\"]\n            and next_port == DEFAULT_SCHEME_PORTS[\"https\"]\n        )\n        if same_origin or https_upgrade:\n            return headers\n        sensitive = {\"authorization\", \"proxy-authorization\", \"cookie\"}\n        return {k: v for k, v in headers.items() if k.lower() not in sensitive}\n\n    @staticmethod\n    def _resolve_byte_limit(env_var: str, setting_name: str, fallback: int) -> int:\n        \"\"\"Resolve one byte cap from settings, or the environment when no service is available.\n\n        The settings service applies Langflow's normal configuration precedence. Reading the\n        process environment directly is only a fallback for standalone ``lfx`` execution.\n        \"\"\"\n        try:\n            settings_service = get_settings_service()\n        except Exception:  # noqa: BLE001 - service registry may not be ready\n            settings_service = None\n        if settings_service is not None:\n            return getattr(settings_service.settings, setting_name, fallback)\n\n        env_value = os.getenv(env_var)\n        if env_value is not None:\n            try:\n                value = int(env_value)\n            except ValueError:\n                logger.warning(f\"Ignoring invalid {env_var}={env_value!r}; using {fallback}\")\n                return fallback\n            if value > 0:\n                return value\n            logger.warning(f\"Ignoring non-positive {env_var}={env_value!r}; using {fallback}\")\n            return fallback\n\n        return fallback\n\n    @classmethod\n    def _resolve_byte_limits(cls) -> tuple[int, int]:\n        \"\"\"Read the configurable per-response and total byte caps for the current fetch.\"\"\"\n        return (\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_RESPONSE_BYTES\",\n                \"url_component_max_response_bytes\",\n                FALLBACK_MAX_RESPONSE_BYTES,\n            ),\n            cls._resolve_byte_limit(\n                \"LANGFLOW_URL_COMPONENT_MAX_TOTAL_BYTES\",\n                \"url_component_max_total_bytes\",\n                FALLBACK_MAX_TOTAL_BYTES,\n            ),\n        )\n\n    async def _read_bounded_text(self, response: httpx.Response) -> str:\n        \"\"\"Read a streamed response body without exceeding the per-response or total byte budget.\n\n        Encoded and decoded bytes are each bounded. The larger count is charged against the\n        total budget even when the body is rejected, so a server that keeps answering with\n        oversized bodies cannot make a crawl download without bound. Decoder errors charge\n        the reserved output bound because zlib may discard output before raising.\n\n        Raises:\n            httpx.HTTPError: If the body is (or declares to be) larger than the remaining budget,\n                or uses a content coding whose decompression cannot be bounded.\n        \"\"\"\n        codings = [c.strip().lower() for c in response.headers.get(\"content-encoding\", \"\").split(\",\")]\n        codings = [c for c in codings if c not in {\"\", \"identity\"}]\n        if len(codings) > 1 or (codings and codings[0] not in ALLOWED_CONTENT_ENCODINGS):\n            msg = f\"Unsupported content encoding from {response.url}: {', '.join(codings)}\"\n            raise httpx.HTTPError(msg)\n\n        limit = min(self._max_response_bytes, self._bytes_remaining)\n        too_large = f\"Response from {response.url} exceeds the {limit} byte limit\"\n        declared = response.headers.get(\"content-length\", \"\")\n        if declared.isdigit() and int(declared) > limit:\n            raise httpx.HTTPError(too_large)\n\n        body = bytearray()\n        raw_bytes = decoded_bytes = 0\n        coding = codings[0] if codings else None\n        decompressor = (\n            zlib.decompressobj(zlib.MAX_WBITS | 16 if coding == \"gzip\" else zlib.MAX_WBITS) if coding else None\n        )\n        first_decode = True\n        deflate_prefix = b\"\"\n        try:\n            # HTTPX's automatic decoder can allocate unbounded output or retain an\n            # endless tail in zlib.unused_data without yielding any decoded bytes.\n            async for raw_chunk in response.aiter_raw():\n                raw_bytes += len(raw_chunk)\n                if raw_bytes > limit:\n                    raise httpx.HTTPError(too_large)\n                chunk = raw_chunk\n                if decompressor is not None:\n                    if decompressor.eof:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    if coding == \"deflate\" and first_decode:\n                        # HTTP deflate may omit the zlib wrapper. Wait for enough\n                        # header bytes before trying the same fallback as HTTPX.\n                        chunk = deflate_prefix + chunk\n                        if len(chunk) < ZLIB_HEADER_BYTES:\n                            deflate_prefix = chunk\n                            continue\n                    max_output = limit - decoded_bytes + 1\n                    try:\n                        try:\n                            decoded = decompressor.decompress(chunk, max_output)\n                        except zlib.error:\n                            if coding != \"deflate\" or not first_decode:\n                                raise\n                            decompressor = zlib.decompressobj(-zlib.MAX_WBITS)\n                            decoded = decompressor.decompress(chunk, max_output)\n                    except zlib.error as exc:\n                        # zlib can produce output before detecting a bad checksum,\n                        # then raise without returning it. Charge the reserved bound.\n                        decoded_bytes = limit + 1\n                        msg = f\"Invalid compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg) from exc\n                    first_decode = False\n                    decoded_bytes += len(decoded)\n                    if decoded_bytes > limit:\n                        raise httpx.HTTPError(too_large)\n                    if decompressor.unused_data:\n                        msg = f\"Trailing data after compressed response from {response.url}\"\n                        raise httpx.DecodingError(msg)\n                    chunk = decoded\n                else:\n                    decoded_bytes += len(chunk)\n                # Check decoded output before retaining it in the response body.\n                body.extend(chunk)\n            if decompressor is not None and not decompressor.eof:\n                msg = f\"Incomplete compressed response from {response.url}\"\n                raise httpx.DecodingError(msg)\n        finally:\n            # Charge encoded overhead as well as expanded output, including rejected\n            # responses. Neither representation may bypass the shared fetch budget.\n            self._bytes_remaining -= max(raw_bytes, decoded_bytes)\n        # Same character decoding as ``response.text``. A complete zlib stream has\n        # already emitted all its output, so no unbounded decoder flush is needed.\n        return body.decode(response.encoding or \"utf-8\", errors=\"replace\")\n\n    async def _process_response(self, response: httpx.Response) -> tuple[str, dict]:\n        \"\"\"Turn a final (non-redirect) streamed response into its HTML content and metadata.\n\n        Args:\n            response: The HTTP response to process, opened with ``stream=True``\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        if self.check_response_status:\n            response.raise_for_status()\n\n        content_type = response.headers.get(\"content-type\", \"\").lower()\n\n        # Filter out CSS files if requested\n        if self.filter_text_html and \"text/css\" in content_type:\n            logger.debug(f\"Skipping CSS file: {response.url}\")\n            return \"\", {}\n\n        # Get the HTML content\n        html_content = await self._read_bounded_text(response)\n\n        # Extract metadata\n        metadata = {\n            \"source\": str(response.url),\n            \"title\": \"\",\n            \"description\": \"\",\n            \"content_type\": content_type,\n            \"language\": \"\",\n        }\n\n        # Try to extract title and description from HTML\n        try:\n            soup = BeautifulSoup(html_content, \"lxml\")\n        except Exception:  # noqa: BLE001\n            # Broad exception is acceptable here - metadata extraction is optional\n            # and we don't want to fail the entire request if it fails\n            logger.debug(f\"Failed to extract metadata from {response.url}\")\n        else:\n            if soup.title:\n                metadata[\"title\"] = soup.title.string or \"\"\n\n            # Try to get description from meta tags\n            meta_desc = soup.find(\"meta\", attrs={\"name\": \"description\"})\n            if meta_desc and meta_desc.get(\"content\"):\n                metadata[\"description\"] = meta_desc[\"content\"]\n\n            # Try to get language\n            html_tag = soup.find(\"html\")\n            if html_tag and html_tag.get(\"lang\"):\n                metadata[\"language\"] = html_tag[\"lang\"]\n\n        return html_content, metadata\n\n    async def _fetch_with_revalidated_redirects(\n        self, url: str, validated_ips: list[str], headers: dict\n    ) -> tuple[str, dict]:\n        \"\"\"Fetch ``url``, following redirects manually and re-validating every hop.\n\n        This closes an SSRF bypass: with redirects enabled, httpx would otherwise\n        auto-follow a redirect from a validated public URL to an internal address that\n        the pinned transport never checked (the redirect target is a different host, so\n        it is not in the pin map). Each redirect ``Location`` is resolved (relative\n        locations included) and re-validated with ``ensure_url`` - the same private/\n        loopback/link-local denylist and DNS pinning applied to the initial request -\n        before any connection to it is made. A blocked hop raises ``ValueError`` (unless\n        ``continue_on_failure``); the chain is capped at ``MAX_REDIRECTS`` hops.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning of the initial URL\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        current_url = url\n        current_ips = validated_ips\n\n        for _ in range(MAX_REDIRECTS + 1):\n            # Stream so redirect bodies are never read and the final body is read bounded.\n            async with (\n                self._build_http_client(current_url, current_ips) as client,\n                client.stream(\n                    \"GET\", current_url, headers=headers, timeout=self.timeout, follow_redirects=False\n                ) as response,\n            ):\n                location = response.headers.get(\"location\")\n                if response.status_code not in REDIRECT_STATUS_CODES or not location:\n                    # Not a redirect (or no Location header) - this is the final response.\n                    return await self._process_response(response)\n\n            # Resolve relative redirects against the current URL.\n            next_url = urljoin(current_url, location)\n\n            # Re-validate the redirect target with the same SSRF denylist + DNS pinning.\n            try:\n                validated_next_url, current_ips = self.ensure_url(next_url)\n            except (ValueError, SSRFProtectionError) as e:\n                if self.continue_on_failure:\n                    logger.warning(f\"Skipping blocked or invalid redirect to {next_url}: {e}\")\n                    return \"\", {}\n                msg = f\"SSRF Protection: blocked redirect to {next_url}: {e}\"\n                raise ValueError(msg) from e\n\n            headers = self._headers_for_redirect(headers, current_url, next_url)\n            current_url = validated_next_url\n\n        # Exhausted the redirect budget.\n        if self.continue_on_failure:\n            logger.warning(f\"Exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\")\n            return \"\", {}\n        msg = f\"SSRF Protection: exceeded the maximum of {MAX_REDIRECTS} redirects while requesting {url}\"\n        raise ValueError(msg)\n\n    async def _fetch_url_with_pinning(self, url: str, validated_ips: list[str], headers: dict) -> tuple[str, dict]:\n        \"\"\"Fetch a single URL with DNS pinning protection, following redirects safely.\n\n        Args:\n            url: The URL to fetch\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n\n        Returns:\n            tuple[str, dict]: The HTML content and metadata\n        \"\"\"\n        try:\n            # When SSRF protection is enabled we must follow redirects manually so each hop\n            # is re-validated and DNS-pinned; letting httpx auto-follow would connect to the\n            # redirect target without pinning (different host, not in the pin map) and re-open\n            # the SSRF hole that DNS pinning closes. With protection disabled there is no pin\n            # to bypass, so redirects follow the requests httpx itself builds for each hop.\n            if self.follow_redirects and is_ssrf_protection_enabled():\n                return await self._fetch_with_revalidated_redirects(url, validated_ips, headers)\n\n            async with self._build_http_client(url, validated_ips) as client:\n                request = client.build_request(\"GET\", url, headers=headers, timeout=self.timeout)\n                # httpx auto-follow reads every redirect body into memory without a limit, so hop\n                # manually via ``next_request`` (same cookies, headers and redirect cap as httpx).\n                for _ in range(MAX_REDIRECTS + 1):\n                    response = await client.send(request, stream=True, follow_redirects=False)\n                    try:\n                        if not (self.follow_redirects and response.next_request):\n                            return await self._process_response(response)\n                        request = response.next_request\n                    finally:\n                        await response.aclose()\n            msg = \"Exceeded maximum allowed redirects.\"\n            raise httpx.TooManyRedirects(msg, request=request)\n\n        except httpx.HTTPError as e:\n            if self.continue_on_failure:\n                logger.warning(f\"Failed to fetch {url}: {e}\")\n                return \"\", {}\n            raise\n\n    async def _crawl_recursive(\n        self, start_url: str, validated_ips: list[str], headers: dict, visited: set, depth: int = 0\n    ) -> list[dict]:\n        \"\"\"Recursively crawl URLs with DNS pinning protection.\n\n        Args:\n            start_url: The URL to start crawling from\n            validated_ips: Validated IPs for DNS pinning\n            headers: HTTP headers to send\n            visited: Set of already visited URLs\n            depth: Current crawl depth\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n        \"\"\"\n        if depth >= self.max_depth or start_url in visited:\n            return []\n        if self._bytes_remaining <= 0:\n            logger.warning(f\"Skipping {start_url}: reached the {self._max_total_bytes} byte limit for this fetch\")\n            return []\n\n        visited.add(start_url)\n        documents = []\n\n        # Fetch the current URL\n        html_content, metadata = await self._fetch_url_with_pinning(start_url, validated_ips, headers)\n\n        # Redirects may have landed on a different canonical URL (http->https, www\n        # normalization). Resolve relative links and the prevent_outside check against\n        # the URL the content actually came from, and mark it visited so links back to\n        # the canonical form are not re-crawled.\n        base_url = metadata.get(\"source\") or start_url\n        visited.add(base_url)\n\n        if not html_content:\n            return documents\n\n        # Extract content based on format\n        extractors = {\n            \"HTML\": self._html_extractor,\n            \"Markdown\": self._markdown_extractor,\n            \"Text\": self._text_extractor,\n        }\n        extractor = extractors.get(self.format, self._text_extractor)\n        extracted_content = extractor(html_content)\n\n        # Add the document\n        documents.append(\n            {\n                \"page_content\": extracted_content,\n                \"metadata\": metadata,\n            }\n        )\n\n        # If we haven't reached max depth, extract and follow links\n        if depth < self.max_depth - 1:\n            try:\n                soup = BeautifulSoup(html_content, \"lxml\")\n                links = soup.find_all(\"a\", href=True)\n\n                for link in links:\n                    if self._bytes_remaining <= 0:\n                        break\n                    href = link[\"href\"]\n                    # Resolve relative URLs\n                    absolute_url = urljoin(base_url, href)\n\n                    # Skip if already visited\n                    if absolute_url in visited:\n                        continue\n\n                    # Check if we should prevent going outside the domain\n                    if self.prevent_outside:\n                        start_domain = urlparse(base_url).netloc\n                        link_domain = urlparse(absolute_url).netloc\n                        if start_domain != link_domain:\n                            continue\n\n                    # Validate and crawl the linked URL\n                    try:\n                        _, link_validated_ips = self.ensure_url(absolute_url)\n                        sub_docs = await self._crawl_recursive(\n                            absolute_url, link_validated_ips, headers, visited, depth + 1\n                        )\n                        documents.extend(sub_docs)\n                    except (ValueError, SSRFProtectionError) as e:\n                        if self.continue_on_failure:\n                            logger.warning(f\"Skipping {absolute_url}: {e}\")\n                            continue\n                        raise\n\n            except Exception:  # noqa: BLE001\n                # Broad exception is acceptable here - link extraction is optional\n                # and we don't want to fail the entire crawl if one page has issues\n                logger.debug(f\"Failed to extract links from {start_url}\")\n\n        return documents\n\n    async def fetch_url_contents(self) -> list[dict]:\n        \"\"\"Load documents from the configured URLs with SSRF protection and DNS pinning.\n\n        This method implements comprehensive SSRF (Server-Side Request Forgery) protection\n        using DNS pinning to prevent DNS rebinding attacks. Each URL is validated and its\n        DNS resolution is pinned before any HTTP requests are made.\n\n        Returns:\n            list[dict]: List of documents with content and metadata\n\n        Raises:\n            ValueError: If no valid URLs are provided or if there's an error loading documents\n        \"\"\"\n        self._max_response_bytes, self._max_total_bytes = self._resolve_byte_limits()\n        self._bytes_remaining = self._max_total_bytes\n        try:\n            # Validate all URLs and get their validated IPs for DNS pinning\n            validated_urls = []\n            first_validation_error: Exception | None = None\n            for url in self.urls:\n                if not url.strip():\n                    continue\n                try:\n                    normalized_url, validated_ips = self.ensure_url(url)\n                    validated_urls.append((normalized_url, validated_ips))\n                except (ValueError, SSRFProtectionError) as e:\n                    # Remember the first rejection so its real cause (for example an\n                    # SSRF block) can be surfaced if no URL survives validation,\n                    # instead of being hidden behind \"No valid URLs provided\".\n                    if first_validation_error is None:\n                        first_validation_error = e\n                    if self.continue_on_failure:\n                        logger.warning(f\"Skipping invalid URL {url}: {e}\")\n                        continue\n                    raise\n\n            # Remove duplicates while preserving order\n            seen = set()\n            unique_validated_urls = []\n            for url, ips in validated_urls:\n                if url not in seen:\n                    seen.add(url)\n                    unique_validated_urls.append((url, ips))\n\n            logger.debug(f\"Validated {len(unique_validated_urls)} unique URL(s)\")\n\n            if not unique_validated_urls:\n                # Surface the actual reason every candidate URL was rejected (e.g. an\n                # SSRF block) rather than a generic message, so a security failure is\n                # not silently swallowed when continue_on_failure is enabled.\n                if first_validation_error is not None:\n                    raise first_validation_error\n                msg = \"No valid URLs provided.\"\n                raise ValueError(msg)\n\n            # Prepare headers\n            headers_dict = {header[\"key\"]: header[\"value\"] for header in self.headers if header[\"value\"] is not None}\n\n            # Crawl all URLs\n            all_docs = []\n            for url, validated_ips in unique_validated_urls:\n                logger.debug(f\"Crawling {url} with max_depth={self.max_depth}\")\n\n                try:\n                    visited = set()\n                    docs = await self._crawl_recursive(url, validated_ips, headers_dict, visited, depth=0)\n\n                    if not docs:\n                        logger.warning(f\"No documents found for {url}\")\n                        continue\n\n                    logger.debug(f\"Found {len(docs)} document(s) from {url}\")\n                    all_docs.extend(docs)\n\n                except httpx.HTTPError as e:\n                    if self.continue_on_failure:\n                        logger.warning(f\"Error loading documents from {url}: {e}\")\n                        continue\n                    msg = f\"Error loading documents from {url}: {e}\"\n                    raise ValueError(msg) from e\n\n            if not all_docs:\n                msg = \"No documents were successfully loaded from any URL\"\n                raise ValueError(msg)\n\n            # Convert to output format\n            return [\n                {\n                    \"text\": safe_convert(doc[\"page_content\"], clean_data=True),\n                    \"url\": doc[\"metadata\"].get(\"source\", \"\"),\n                    \"title\": doc[\"metadata\"].get(\"title\", \"\"),\n                    \"description\": doc[\"metadata\"].get(\"description\", \"\"),\n                    \"content_type\": doc[\"metadata\"].get(\"content_type\", \"\"),\n                    \"language\": doc[\"metadata\"].get(\"language\", \"\"),\n                }\n                for doc in all_docs\n            ]\n\n        except Exception as e:\n            error_msg = e.message if hasattr(e, \"message\") else str(e)\n            msg = f\"Error loading documents: {error_msg}\"\n            logger.exception(msg)\n            raise ValueError(msg) from e\n\n    async def fetch_content(self) -> DataFrame:\n        \"\"\"Convert the documents to a DataFrame.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return DataFrame(data=url_contents)\n\n    async def fetch_content_as_message(self) -> Message:\n        \"\"\"Convert the documents to a Message.\"\"\"\n        url_contents = await self.fetch_url_contents()\n        return Message(text=\"\\n\\n\".join([x[\"text\"] for x in url_contents]), data={\"data\": url_contents})\n"
             },
             "continue_on_failure": {
               "_input_type": "BoolInput",
@@ -5766,7 +5766,7 @@
           "icon": "file-text",
           "legacy": false,
           "metadata": {
-            "code_hash": "6d53589f01c8",
+            "code_hash": "1bc59574381f",
             "component_class_name": "FileComponent",
             "dependencies": {
               "dependencies": [
@@ -5935,7 +5935,7 @@
               "show": true,
               "title_case": false,
               "type": "code",
-              "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = str(file.path)\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [str(f.path) for f in file_list]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
+              "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities, bounded by `concurrency_multithreading` via `aparallel_load_data`.\n- The loaders are coroutines (see BaseFileComponent): storage reads are awaited on\n  the caller's event loop, parsing and the Docling subprocess run in worker threads.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport contextlib\nimport json\nimport subprocess\nimport sys\nimport textwrap\nimport threading\nimport time\nfrom contextvars import ContextVar\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.storage_utils import (\n    parse_storage_path,\n    read_file_bytes,\n    require_storage_service,\n    validate_image_content_type,\n)\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, aparallel_load_data, aparse_text_file_to_data\nfrom lfx.inputs import SortableListInput\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame  # noqa: TC001\nfrom lfx.schema.message import Message\nfrom lfx.services.deps import get_settings_service, get_storage_service\nfrom lfx.utils.async_helpers import delegates_to, run_until_complete\nfrom lfx.utils.validate_cloud import is_astra_cloud_environment\n\n_FILE_TOOL_CANCEL_EVENT: ContextVar[threading.Event | None] = ContextVar(\"file_tool_cancel_event\", default=None)\n_FILE_TOOL_CANCEL_WAIT_SECONDS = 6\n_FILE_TOOL_MAX_CONCURRENT_LOADS = 4\n_FILE_TOOL_LIMITER_ATTRIBUTE = \"_lfx_file_tool_load_limiter\"\n_FILE_TOOL_PROCESS_REAP_SECONDS = 2\n\n\nclass _FileToolCancelledError(RuntimeError):\n    \"\"\"Stop synchronous file loading after its async tool call is cancelled.\"\"\"\n\n\ndef _raise_if_file_tool_cancelled(cancel_event: threading.Event | None = None) -> None:\n    event = cancel_event or _FILE_TOOL_CANCEL_EVENT.get()\n    if event is not None and event.is_set():\n        raise _FileToolCancelledError\n\n\ndef _kill_and_reap_process(proc: subprocess.Popen) -> None:\n    try:\n        proc.kill()\n    except ProcessLookupError:\n        pass\n    except OSError:\n        logger.exception(\"Failed to kill cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while killing cancelled Docling subprocess\")\n\n    try:\n        proc.communicate(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.warning(\"Timed out draining cancelled Docling subprocess; waiting for process exit\")\n    except OSError:\n        logger.exception(\"Failed to drain cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while draining cancelled Docling subprocess\")\n    else:\n        return\n\n    try:\n        proc.wait(timeout=_FILE_TOOL_PROCESS_REAP_SECONDS)\n    except subprocess.TimeoutExpired:\n        logger.exception(\"Cancelled Docling subprocess could not be reaped within the cleanup timeout\")\n    except OSError:\n        logger.exception(\"Failed to reap cancelled Docling subprocess\")\n    except Exception:  # noqa: BLE001 - cleanup must not mask the caller's cancellation\n        logger.exception(\"Unexpected error while reaping cancelled Docling subprocess\")\n\n\ndef _get_file_tool_limiter() -> asyncio.Semaphore:\n    \"\"\"Return the bounded loader admission gate for the current event loop.\"\"\"\n    loop = asyncio.get_running_loop()\n    limiter = getattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, None)\n    if limiter is None:\n        limiter = asyncio.Semaphore(_FILE_TOOL_MAX_CONCURRENT_LOADS)\n        setattr(loop, _FILE_TOOL_LIMITER_ATTRIBUTE, limiter)\n    return limiter\n\n\ndef _log_abandoned_file_loader_result(task: asyncio.Task) -> None:\n    \"\"\"Observe unexpected failures from a synchronous loader that outlived its caller.\"\"\"\n    try:\n        error = task.exception()\n    except asyncio.CancelledError:\n        return\n    if error is not None and not isinstance(error, _FileToolCancelledError):\n        logger.error(\"Abandoned file loader failed after its caller was cancelled\", exc_info=error)\n\n\ndef _write_temp_file(content: bytes, suffix: str) -> str:\n    with NamedTemporaryFile(mode=\"wb\", suffix=suffix, delete=False) as tmp_file:\n        tmp_file.write(content)\n        return tmp_file.name\n\n\ndef _unlink_orphaned_temp_file(write: asyncio.Future[str]) -> None:\n    \"\"\"Remove a temp file whose writer finished after the awaiting coroutine was cancelled.\"\"\"\n    if write.cancelled() or write.exception() is not None:\n        return\n    with contextlib.suppress(OSError):\n        Path(write.result()).unlink()\n\n\nasync def _wait_for_cancelled_file_tool_loader(loader_task: asyncio.Task) -> None:\n    \"\"\"Give a cancelled tool call's loader time to clean up, without cancelling it.\n\n    This waits with ``asyncio.wait`` rather than ``asyncio.shield``. On Python 3.14, a\n    shield whose outer future is cancelled reports the inner task's eventual exception to\n    the loop's exception handler, which logged the expected ``_FileToolCancelledError`` as\n    an ERROR on every cancelled tool call.\n    \"\"\"\n    try:\n        await asyncio.wait({loader_task}, timeout=_FILE_TOOL_CANCEL_WAIT_SECONDS)\n    finally:\n        if not loader_task.done():\n            loader_task.add_done_callback(_log_abandoned_file_loader_result)\n        elif not loader_task.cancelled():\n            error = loader_task.exception()\n            if error is not None and not isinstance(error, _FileToolCancelledError):\n                logger.error(\"File loader failed while cleaning up a cancelled tool call\", exc_info=error)\n\n\ndef _get_storage_location_options():\n    \"\"\"Get storage location options, filtering out Local if in Astra cloud environment.\"\"\"\n    all_options = [{\"name\": \"AWS\", \"icon\": \"Amazon\"}, {\"name\": \"Google Drive\", \"icon\": \"google\"}]\n    if is_astra_cloud_environment():\n        return all_options\n    return [{\"name\": \"Local\", \"icon\": \"hard-drive\"}, *all_options]\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"Read File\"\n    # description is now a dynamic property - see get_tool_description()\n    _base_description = \"Loads and returns the content from uploaded files.\"\n    documentation: str = \"https://docs.langflow.org/read-file\"\n    icon = \"file-text\"\n    name = \"File\"\n    add_tool_output = True  # Enable tool mode toggle without requiring tool_mode inputs\n\n    # Extensions that can be processed without Docling (using standard text parsing)\n    TEXT_EXTENSIONS = TEXT_FILE_TYPES\n\n    # Extensions that require Docling for processing (images, advanced office formats, etc.)\n    DOCLING_ONLY_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpg\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_EXTENSIONS,\n        *DOCLING_ONLY_EXTENSIONS,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            input_item.tool_mode = False  # Disable tool mode for file upload input\n            input_item.required = False  # Make it optional so it doesn't error in tool mode\n            break\n\n    inputs = [\n        SortableListInput(\n            name=\"storage_location\",\n            display_name=\"Storage Location\",\n            placeholder=\"Select Location\",\n            info=\"Choose where to read the file from.\",\n            options=_get_storage_location_options(),\n            real_time_refresh=True,\n            limit=1,\n            value=[{\"name\": \"Local\", \"icon\": \"hard-drive\"}],\n            advanced=True,\n        ),\n        *_base_inputs,\n        StrInput(\n            name=\"text_encoding\",\n            display_name=\"Text Encoding\",\n            value=\"\",\n            advanced=True,\n            info=(\n                \"Leave blank to detect text encoding automatically. If accents or punctuation look incorrect, \"\n                \"enter the file's encoding, such as cp1252 or utf-8. Applies to standard text parsing and CSV, \"\n                \"not PDF, DOCX, or the Advanced Parser. The selected encoding is shown in logs and output metadata.\"\n            ),\n        ),\n        StrInput(\n            name=\"file_path_str\",\n            display_name=\"File Path\",\n            info=(\n                \"Path to the file to read. Used when component is called as a tool. \"\n                \"If not provided, will use the uploaded file from 'path' input.\"\n            ),\n            show=False,\n            advanced=True,\n            tool_mode=True,  # Required for Toolset toggle, but _get_tools() ignores this parameter\n            required=False,\n        ),\n        # AWS S3 specific inputs\n        SecretStrInput(\n            name=\"aws_access_key_id\",\n            display_name=\"AWS Access Key ID\",\n            info=\"AWS Access key ID.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"aws_secret_access_key\",\n            display_name=\"AWS Secret Key\",\n            info=\"AWS Secret Key.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"bucket_name\",\n            display_name=\"S3 Bucket Name\",\n            info=\"Enter the name of the S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"aws_region\",\n            display_name=\"AWS Region\",\n            info=\"AWS region (e.g., us-east-1, eu-west-1).\",\n            show=False,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"s3_file_key\",\n            display_name=\"S3 File Key\",\n            info=\"The key (path) of the file in S3 bucket.\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        # Google Drive specific inputs\n        SecretStrInput(\n            name=\"service_account_key\",\n            display_name=\"GCP Credentials Secret Key\",\n            info=\"Your Google Cloud Platform service account JSON key as a secret string (complete JSON content).\",\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"file_id\",\n            display_name=\"Google Drive File ID\",\n            info=(\"The Google Drive file ID to read. The file must be shared with the service account email.\"),\n            show=False,\n            advanced=False,\n            required=True,\n        ),\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            # Disabled in cloud\n            show=not is_astra_cloud_environment(),\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\", tool_mode=True),\n    ]\n\n    # ------------------------------ Tool description with file names --------------\n\n    def get_tool_description(self) -> str:\n        \"\"\"Return a dynamic description that includes the names of uploaded files.\n\n        This helps the Agent understand which files are available to read.\n        \"\"\"\n        base_description = type(self)._base_description  # noqa: SLF001\n\n        # Get the list of uploaded file paths\n        file_paths = getattr(self, \"path\", None)\n        if not file_paths:\n            return base_description\n\n        # Ensure it's a list\n        if not isinstance(file_paths, list):\n            file_paths = [file_paths]\n\n        # Extract just the file names from the paths\n        file_names = []\n        for fp in file_paths:\n            if fp:\n                name = Path(fp).name\n                file_names.append(name)\n\n        if file_names:\n            files_str = \", \".join(file_names)\n            return f\"{base_description} Available files: {files_str}. Call this tool to read these files.\"\n\n        return base_description\n\n    @property\n    def description(self) -> str:\n        \"\"\"Dynamic description property that includes uploaded file names.\"\"\"\n        return self.get_tool_description()\n\n    async def _get_tools(self) -> list:\n        \"\"\"Override to create a tool without parameters.\n\n        The Read File component should use the files already uploaded via UI,\n        not accept file paths from the Agent (which wouldn't know the internal paths).\n        \"\"\"\n        from langchain_core.tools import StructuredTool\n        from pydantic import BaseModel\n\n        # Empty schema - no parameters needed\n        class EmptySchema(BaseModel):\n            \"\"\"No parameters required - uses pre-uploaded files.\"\"\"\n\n        async def read_files_tool() -> str:\n            \"\"\"Read the content of uploaded files.\"\"\"\n            cancel_event = threading.Event()\n            cancel_token = _FILE_TOOL_CANCEL_EVENT.set(cancel_event)\n            try:\n                if getattr(self, \"advanced_mode\", False):\n                    # In advanced mode, use the markdown output path so that the\n                    # tool shares the same Docling processing as the advanced\n                    # outputs rather than triggering a second subprocess via\n                    # load_files_message.\n                    self.markdown = True\n                    loader_name = \"load_files_markdown\"\n                else:\n                    loader_name = \"load_files_message\"\n                # Await the async loader chain on this loop: storage reads are awaited here,\n                # while parsing and the Docling subprocess run in worker threads, so\n                # streaming/heartbeats on the same loop keep flowing while the agent waits.\n                # A subclass that overrides the sync loader still has it run in a thread.\n                # Keep admission bounded until the real work exits so cancelled parsers\n                # cannot build an unbounded default-executor backlog.\n                load_limiter = _get_file_tool_limiter()\n                await load_limiter.acquire()\n                try:\n                    loader_task = asyncio.create_task(self._adispatch(loader_name))\n                except BaseException:\n                    load_limiter.release()\n                    raise\n                loader_task.add_done_callback(lambda _task: load_limiter.release())\n                try:\n                    # asyncio.wait neither cancels the loader nor leaves an orphaned\n                    # shield behind to report its outcome if this call is cancelled.\n                    await asyncio.wait({loader_task})\n                    # A cancelled loader task raises CancelledError here while its\n                    # worker thread keeps running, so it takes the same path below.\n                    result = loader_task.result()\n                except asyncio.CancelledError:\n                    cancel_event.set()\n                    # Give cooperative cleanup time to kill/reap Docling and remove\n                    # temporary files before releasing this tool invocation.\n                    await _wait_for_cancelled_file_tool_loader(loader_task)\n                    raise\n                if hasattr(result, \"get_text\"):\n                    return result.get_text()\n                if hasattr(result, \"text\"):\n                    return result.text\n                return str(result)\n            except (FileNotFoundError, ValueError, OSError, RuntimeError) as e:\n                return f\"Error reading files: {e}\"\n            finally:\n                _FILE_TOOL_CANCEL_EVENT.reset(cancel_token)\n\n        description = self.get_tool_description()\n\n        tool = StructuredTool(\n            name=\"load_files_message\",\n            description=description,\n            coroutine=read_files_tool,\n            args_schema=EmptySchema,\n            handle_tool_error=True,\n            tags=[\"load_files_message\"],\n            metadata={\n                \"display_name\": \"Read File\",\n                \"display_description\": description,\n            },\n        )\n\n        return [tool]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def _disable_docling_fields_in_cloud(self, build_config: dict[str, Any]) -> None:\n        \"\"\"Disable all Docling-related fields in cloud environments.\"\"\"\n        if \"advanced_mode\" in build_config:\n            build_config[\"advanced_mode\"][\"show\"] = False\n            build_config[\"advanced_mode\"][\"value\"] = False\n        # Hide all Docling-related fields\n        docling_fields = (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\")\n        for field in docling_fields:\n            if field in build_config:\n                build_config[field][\"show\"] = False\n        # Also disable OCR engine specifically\n        if \"ocr_engine\" in build_config:\n            build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        # Update storage location options dynamically based on cloud environment\n        if \"storage_location\" in build_config:\n            updated_options = _get_storage_location_options()\n            build_config[\"storage_location\"][\"options\"] = updated_options\n\n        # Handle storage location selection\n        if field_name == \"storage_location\":\n            # Extract selected storage location\n            selected = [location[\"name\"] for location in field_value] if isinstance(field_value, list) else []\n\n            # Hide all storage-specific fields first\n            storage_fields = [\n                \"aws_access_key_id\",\n                \"aws_secret_access_key\",\n                \"bucket_name\",\n                \"aws_region\",\n                \"s3_file_key\",\n                \"service_account_key\",\n                \"file_id\",\n            ]\n\n            for f_name in storage_fields:\n                if f_name in build_config:\n                    build_config[f_name][\"show\"] = False\n\n            # Show fields based on selected storage location\n            if len(selected) == 1:\n                location = selected[0]\n\n                if location == \"Local\":\n                    # Show file upload input for local storage\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = True\n\n                elif location == \"AWS\":\n                    # Hide file upload input, show AWS fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    aws_fields = [\n                        \"aws_access_key_id\",\n                        \"aws_secret_access_key\",\n                        \"bucket_name\",\n                        \"aws_region\",\n                        \"s3_file_key\",\n                    ]\n                    for f_name in aws_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n\n                elif location == \"Google Drive\":\n                    # Hide file upload input, show Google Drive fields\n                    if \"path\" in build_config:\n                        build_config[\"path\"][\"show\"] = False\n\n                    gdrive_fields = [\"service_account_key\", \"file_id\"]\n                    for f_name in gdrive_fields:\n                        if f_name in build_config:\n                            build_config[f_name][\"show\"] = True\n                            build_config[f_name][\"advanced\"] = False\n            # No storage location selected - show file upload by default\n            elif \"path\" in build_config:\n                build_config[\"path\"][\"show\"] = True\n\n            return build_config\n\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n\n            # Disable in cloud environments\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                # If all files can be processed by docling, do so\n                allow_advanced = all(not file_path.endswith((\".csv\", \".xlsx\", \".parquet\")) for file_path in paths)\n                build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n                if not allow_advanced:\n                    build_config[\"advanced_mode\"][\"value\"] = False\n                    docling_fields = (\n                        \"pipeline\",\n                        \"ocr_engine\",\n                        \"doc_key\",\n                        \"md_image_placeholder\",\n                        \"md_page_break_placeholder\",\n                    )\n                    for field in docling_fields:\n                        if field in build_config:\n                            build_config[field][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            # Disable in cloud environments - don't show Docling fields even if advanced_mode is toggled\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            else:\n                docling_fields = (\n                    \"pipeline\",\n                    \"ocr_engine\",\n                    \"doc_key\",\n                    \"md_image_placeholder\",\n                    \"md_page_break_placeholder\",\n                )\n                for field in docling_fields:\n                    if field in build_config:\n                        build_config[field][\"show\"] = bool(field_value)\n                        if field == \"pipeline\":\n                            build_config[field][\"advanced\"] = not bool(field_value)\n\n        elif field_name == \"pipeline\":\n            # Disable in cloud environments - don't show OCR engine even if pipeline is changed\n            if is_astra_cloud_environment():\n                self._disable_docling_fields_in_cloud(build_config)\n            elif field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"dataframe\",\n                        method=\"load_files_structured\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Content\",\n                        name=\"json\",\n                        method=\"load_files_json\",\n                        types=[\"JSON\"],\n                        selected=\"JSON\",\n                        tool_mode=True,\n                    ),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Structured Output\",\n                        name=\"advanced_dataframe\",\n                        method=\"load_files_dataframe\",\n                        types=[\"Table\"],\n                        selected=\"Table\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Markdown\",\n                        name=\"advanced_markdown\",\n                        method=\"load_files_markdown\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Raw Content\",\n                        name=\"message\",\n                        method=\"load_files_message\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"File Path\",\n                        name=\"path\",\n                        method=\"load_files_path\",\n                        types=[\"Message\"],\n                        selected=\"Message\",\n                        tool_mode=True,\n                    ),\n                )\n        else:\n            # Multiple files => DataFrame and Message outputs; advanced parser disabled\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Files\",\n                    name=\"dataframe\",\n                    method=\"load_files\",\n                    types=[\"Table\"],\n                    selected=\"Table\",\n                    tool_mode=True,\n                )\n            )\n            frontend_node[\"outputs\"].append(\n                Output(\n                    display_name=\"Raw Content\",\n                    name=\"message\",\n                    method=\"load_files_message\",\n                    types=[\"Message\"],\n                    selected=\"Message\",\n                    tool_mode=True,\n                )\n            )\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _get_selected_storage_location(self) -> str:\n        \"\"\"Get the selected storage location from the SortableListInput.\"\"\"\n        if hasattr(self, \"storage_location\") and self.storage_location:\n            if isinstance(self.storage_location, list) and len(self.storage_location) > 0:\n                return self.storage_location[0].get(\"name\", \"\")\n            if isinstance(self.storage_location, dict):\n                return self.storage_location.get(\"name\", \"\")\n        return \"Local\"  # Default to Local if not specified\n\n    def _validate_and_resolve_paths(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Override to handle file_path_str input from tool mode and cloud storage.\n\n        Priority:\n        1. Cloud storage (AWS/Google Drive) if selected\n        2. file_path_str (if provided by the tool call)\n        3. path (uploaded file from UI)\n        \"\"\"\n        storage_location = self._get_selected_storage_location()\n\n        # Handle AWS S3\n        if storage_location == \"AWS\":\n            return self._read_from_aws_s3()\n\n        # Handle Google Drive\n        if storage_location == \"Google Drive\":\n            return self._read_from_google_drive()\n\n        # Handle Local storage\n        # Check if file_path_str is provided (from tool mode)\n        file_path_str = getattr(self, \"file_path_str\", None)\n        if file_path_str:\n            # Use the string path from tool mode\n            from pathlib import Path\n\n            from lfx.schema.data import Data\n            from lfx.utils.file_path_security import (\n                StorageNamespaceError,\n                component_file_access_scopes,\n                enforce_local_file_access,\n            )\n\n            # Use same resolution logic as BaseFileComponent (support storage paths)\n            path_str = str(file_path_str)\n            if parse_storage_path(path_str):\n                try:\n                    resolved_path = Path(self.get_full_path(path_str))\n                except StorageNamespaceError:\n                    # A storage namespace outside this graph's scope is an access denial,\n                    # not a resolution failure: never retry it as a plain local path.\n                    raise\n                except (ValueError, AttributeError):\n                    resolved_path = Path(self.resolve_path(path_str))\n            else:\n                resolved_path = Path(self.resolve_path(path_str))\n\n            # Security: confine tool-mode reads to the storage dir in restricted (multi-tenant)\n            # mode so a tenant cannot read arbitrary server files via file_path_str.\n            resolved_path = enforce_local_file_access(resolved_path, scope_ids=component_file_access_scopes(self))\n\n            if not resolved_path.exists():\n                msg = f\"File or directory not found: {file_path_str}\"\n                self.log(msg)\n                if not self.silent_errors:\n                    raise ValueError(msg)\n                return []\n\n            data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(resolved_path)})\n            return [BaseFileComponent.BaseFile(data_obj, resolved_path, delete_after_processing=False)]\n\n        # Otherwise use the default implementation (uses path FileInput)\n        return super()._validate_and_resolve_paths()\n\n    def _read_from_aws_s3(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from AWS S3.\"\"\"\n        from lfx.base.data.cloud_storage_utils import create_s3_client, validate_aws_credentials\n\n        # Validate AWS credentials\n        validate_aws_credentials(self)\n        if not getattr(self, \"s3_file_key\", None):\n            msg = \"S3 File Key is required\"\n            raise ValueError(msg)\n\n        # Create S3 client\n        s3_client = create_s3_client(self)\n\n        # Download file to temp location\n        import tempfile\n\n        # Get file extension from S3 key\n        file_extension = Path(self.s3_file_key).suffix or \"\"\n\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                s3_client.download_fileobj(self.bucket_name, self.s3_file_key, temp_file)\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from S3: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _read_from_google_drive(self) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Read file from Google Drive.\"\"\"\n        import tempfile\n\n        from googleapiclient.http import MediaIoBaseDownload\n\n        from lfx.base.data.cloud_storage_utils import create_google_drive_service\n\n        # Validate Google Drive credentials\n        if not getattr(self, \"service_account_key\", None):\n            msg = \"GCP Credentials Secret Key is required for Google Drive storage\"\n            raise ValueError(msg)\n        if not getattr(self, \"file_id\", None):\n            msg = \"Google Drive File ID is required\"\n            raise ValueError(msg)\n\n        # Create Google Drive service with read-only scope\n        drive_service = create_google_drive_service(\n            self.service_account_key, scopes=[\"https://www.googleapis.com/auth/drive.readonly\"]\n        )\n\n        # Get file metadata to determine file name and extension\n        try:\n            file_metadata = drive_service.files().get(fileId=self.file_id, fields=\"name,mimeType\").execute()\n            file_name = file_metadata.get(\"name\", \"download\")\n        except Exception as e:\n            msg = (\n                f\"Unable to access file with ID '{self.file_id}'. \"\n                f\"Error: {e!s}. \"\n                \"Please ensure: 1) The file ID is correct, 2) The file exists, \"\n                \"3) The service account has been granted access to this file.\"\n            )\n            raise ValueError(msg) from e\n\n        # Download file to temp location\n        file_extension = Path(file_name).suffix or \"\"\n        temp_file_path: str | None = None\n        try:\n            with tempfile.NamedTemporaryFile(mode=\"wb\", suffix=file_extension, delete=False) as temp_file:\n                temp_file_path = temp_file.name\n                request = drive_service.files().get_media(fileId=self.file_id)\n                downloader = MediaIoBaseDownload(temp_file, request)\n                done = False\n                while not done:\n                    _status, done = downloader.next_chunk()\n        except Exception as e:\n            # The context manager has already closed the handle by the time this runs, which is\n            # what Windows requires before the partial download can be deleted. Keeping the\n            # cleanup here also covers a failure raised by the closing flush itself.\n            if temp_file_path is not None:\n                with contextlib.suppress(OSError):\n                    Path(temp_file_path).unlink()\n            msg = f\"Failed to download file from Google Drive: {e}\"\n            raise RuntimeError(msg) from e\n\n        # Create BaseFile object\n        from lfx.schema.data import Data\n\n        temp_path = Path(temp_file_path)\n        data_obj = Data(data={self.SERVER_FILE_PATH_FIELDNAME: str(temp_path)})\n        return [\n            BaseFileComponent.BaseFile(\n                data_obj,\n                temp_path,\n                delete_after_processing=True,\n                cleanup_local_file=True,\n            )\n        ]\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpg\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    async def _get_local_file_for_docling(\n        self, file_path: str, *, is_local_temp_file: bool = False\n    ) -> tuple[str, bool]:\n        \"\"\"Get a local file path for Docling processing, downloading from S3 if needed.\n\n        Args:\n            file_path: Either a local path or S3 key (format \"flow_id/filename\")\n            is_local_temp_file: Whether file_path is an internally created local temporary file.\n\n        Returns:\n            tuple[str, bool]: (local_path, should_delete) where should_delete indicates\n                              if this is a temporary file that should be cleaned up\n        \"\"\"\n        if is_local_temp_file:\n            local_temp_path = Path(file_path)\n            if not local_temp_path.is_absolute() or not local_temp_path.is_file():\n                msg = f\"Invalid local temporary path: {file_path}\"\n                raise ValueError(msg)\n            return file_path, False\n\n        settings = get_settings_service().settings\n        if settings.storage_type == \"local\":\n            return file_path, False\n\n        # S3 storage - download to temp file\n        parsed = parse_storage_path(file_path)\n        if not parsed:\n            msg = f\"Invalid S3 path format: {file_path}. Expected 'flow_id/filename'\"\n            raise ValueError(msg)\n\n        storage_service = require_storage_service(get_storage_service())\n        flow_id, filename = parsed\n\n        # Get file content from S3\n        content = await storage_service.get_file(flow_id, filename)\n\n        write = asyncio.ensure_future(asyncio.to_thread(_write_temp_file, content, Path(filename).suffix))\n        try:\n            temp_path = await asyncio.shield(write)\n        except asyncio.CancelledError:\n            write.add_done_callback(_unlink_orphaned_temp_file)\n            raise\n\n        return temp_path, True\n\n    def _process_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Sync wrapper around ``_aprocess_docling_in_subprocess``.\"\"\"\n        return run_until_complete(\n            self._aprocess_docling_in_subprocess(file_path, is_local_temp_file=is_local_temp_file)\n        )\n\n    async def _aprocess_docling_in_subprocess(self, file_path: str, *, is_local_temp_file: bool = False) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n\n        For S3 storage, the file is downloaded to a temp file first; the download is\n        awaited, and the subprocess is driven from a worker thread.\n        \"\"\"\n        if not file_path:\n            return None\n\n        _raise_if_file_tool_cancelled()\n        settings = get_settings_service().settings\n        if settings.storage_type == \"s3\":\n            local_path, should_delete = await self._get_local_file_for_docling(\n                file_path, is_local_temp_file=is_local_temp_file\n            )\n        else:\n            local_path = file_path\n            should_delete = False\n\n        # The worker owns a downloaded temp file: if this coroutine is cancelled mid-parse the\n        # subprocess may still be reading it, so only the worker may delete it.\n        work = asyncio.create_task(\n            asyncio.to_thread(self._process_docling_local_file, local_path, file_path, should_delete=should_delete)\n        )\n        try:\n            return await asyncio.shield(work)\n        except asyncio.CancelledError:\n            work.add_done_callback(_log_abandoned_file_loader_result)\n            raise\n\n    def _process_docling_local_file(\n        self, local_path: str, original_file_path: str, *, should_delete: bool\n    ) -> Data | None:\n        try:\n            _raise_if_file_tool_cancelled()\n            return self._process_docling_subprocess_impl(local_path, original_file_path)\n        finally:\n            # Clean up temp file if we created one\n            if should_delete:\n                with contextlib.suppress(Exception):\n                    Path(local_path).unlink()  # Ignore cleanup errors\n\n    def _process_docling_subprocess_impl(self, local_file_path: str, original_file_path: str) -> Data | None:\n        \"\"\"Implementation of Docling subprocess processing.\n\n        Args:\n            local_file_path: Path to local file to process\n            original_file_path: Original file path to include in metadata\n        Returns:\n            Data object with processed content\n        \"\"\"\n        args: dict[str, Any] = {\n            \"file_path\": local_file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception as e:\n                    raise e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.datamodel.pipeline_options import VlmPipelineOptions\n                        from docling.datamodel.vlm_model_specs import GRANITEDOCLING_MLX, GRANITEDOCLING_TRANSFORMERS\n                        from docling.document_converter import PdfFormatOption\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n\n                        vl_pipe = VlmPipelineOptions(\n                            vlm_options=GRANITEDOCLING_TRANSFORMERS,\n                        )\n\n                        if sys.platform == \"darwin\":\n                            try:\n                                import mlx_vlm\n                                vl_pipe.vlm_options = GRANITEDOCLING_MLX\n                            except ImportError as e:\n                                raise e\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(\n                            pipeline_cls=VlmPipeline,\n                            pipeline_options=vl_pipe\n                        )\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception as e:\n                        raise e\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    # Extract DoclingDocument metadata\n                    if hasattr(doc, \"name\") and doc.name:\n                        meta[\"name\"] = doc.name\n                    if hasattr(doc, \"origin\") and doc.origin is not None:\n                        origin = doc.origin\n                        if hasattr(origin, \"filename\") and origin.filename:\n                            meta[\"filename\"] = origin.filename\n                        if hasattr(origin, \"binary_hash\") and origin.binary_hash:\n                            meta[\"document_id\"] = str(origin.binary_hash)\n                        if hasattr(origin, \"mimetype\") and origin.mimetype:\n                            meta[\"mimetype\"] = origin.mimetype\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # The path is passed as JSON over stdin to an argument-list subprocess, so shell\n        # metacharacters are ordinary filename characters here.\n        if not isinstance(args[\"file_path\"], str):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        # Use communicate() in bounded intervals so stdout/stderr are drained while the\n        # child runs without losing the heartbeat that keeps the SSE event stream alive.\n        docling_timeout = 600  # 10 minutes; large PDFs with OCR may need this\n        poll_interval = 5  # seconds between progress heartbeats\n\n        proc = subprocess.Popen(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        start = time.monotonic()\n        input_bytes: bytes | None = json.dumps(args).encode(\"utf-8\")\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        try:\n            while True:\n                _raise_if_file_tool_cancelled(cancel_event)\n                elapsed = time.monotonic() - start\n                if elapsed >= docling_timeout:\n                    proc.kill()\n                    proc.communicate()\n                    return Data(\n                        data={\n                            \"error\": (\n                                f\"Docling processing timed out after {docling_timeout}s. \"\n                                \"Consider using the standalone Docling component for large documents.\"\n                            ),\n                            \"file_path\": original_file_path,\n                        },\n                    )\n                try:\n                    stdout_bytes, stderr_bytes = proc.communicate(\n                        input=input_bytes,\n                        timeout=min(poll_interval, docling_timeout - elapsed),\n                    )\n                    _raise_if_file_tool_cancelled(cancel_event)\n                    break\n                except subprocess.TimeoutExpired:\n                    # communicate() retains partially written input and collected output across\n                    # retries, so subsequent calls continue draining without resending stdin.\n                    input_bytes = None\n                    elapsed = time.monotonic() - start\n                    self.log(f\"Docling processing in progress ({int(elapsed)}s elapsed)...\")\n        finally:\n            if (cancel_event is not None and cancel_event.is_set()) or proc.poll() is None:\n                _kill_and_reap_process(proc)\n\n        if not stdout_bytes:\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\") if stderr_bytes else \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": original_file_path})\n\n        try:\n            result = json.loads(stdout_bytes.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = stderr_bytes.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\n                    \"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\",\n                    \"file_path\": original_file_path,\n                },\n            )\n\n        if not result.get(\"ok\"):\n            error_msg = result.get(\"error\", \"Unknown Docling error\")\n            # Override meta file_path with original_file_path to ensure correct path matching\n            meta = result.get(\"meta\", {})\n            meta[\"file_path\"] = original_file_path\n            return Data(data={\"error\": error_msg, **meta})\n\n        meta = result.get(\"meta\", {})\n        # Override meta file_path with original_file_path to ensure correct path matching\n        # The subprocess returns the temp file path, but we need the original S3/local path for rollup_data\n        meta[\"file_path\"] = original_file_path\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    @delegates_to(\"aprocess_files\")\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files. Sync wrapper around ``aprocess_files``.\"\"\"\n        return run_until_complete(self.aprocess_files(file_list))\n\n    async def aprocess_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing, up to ``concurrency_multithreading`` files at once.\n\n        Storage reads are awaited on the running loop; parsing and the Docling subprocess\n        run in worker threads.\n        \"\"\"\n        cancel_event = _FILE_TOOL_CANCEL_EVENT.get()\n        _raise_if_file_tool_cancelled(cancel_event)\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        # Validate image files to detect content/extension mismatches\n        # This prevents API errors like \"Image does not match the provided media type\"\n        image_extensions = {\"jpeg\", \"jpg\", \"png\", \"gif\", \"webp\", \"bmp\", \"tiff\"}\n        settings = get_settings_service().settings\n        for file in file_list:\n            _raise_if_file_tool_cancelled(cancel_event)\n            extension = file.path.suffix[1:].lower()\n            if extension in image_extensions:\n                # Read bytes based on storage type\n                try:\n                    if settings.storage_type == \"s3\":\n                        # For S3 storage, use storage service to read file bytes\n                        file_path_str = file.path.as_posix() if not file.path.is_absolute() else str(file.path)\n                        content = await read_file_bytes(file_path_str)\n                    else:\n                        # For local storage, read bytes directly from filesystem\n                        content = await asyncio.to_thread(file.path.read_bytes)\n\n                    is_valid, error_msg = validate_image_content_type(\n                        str(file.path),\n                        content=content,\n                    )\n                    if not is_valid:\n                        self.log(error_msg)\n                        if not self.silent_errors:\n                            raise ValueError(error_msg)\n                except (OSError, FileNotFoundError) as e:\n                    self.log(f\"Could not read file for validation: {e}\")\n                    # Continue - let it fail later with better error\n\n        # Validate that files requiring Docling are only processed when advanced mode is enabled\n        if not self.advanced_mode:\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                extension = file.path.suffix[1:].lower()\n                if extension in self.DOCLING_ONLY_EXTENSIONS:\n                    if is_astra_cloud_environment():\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Advanced Parser is not available in cloud environments.\"\n                        )\n                    else:\n                        msg = (\n                            f\"File '{file.path.name}' has extension '.{extension}' which requires \"\n                            f\"Advanced Parser mode. Please enable 'Advanced Parser' to process this file.\"\n                        )\n                    self.log(msg)\n                    raise ValueError(msg)\n\n        async def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                _raise_if_file_tool_cancelled(cancel_event)\n                encoding = (getattr(self, \"text_encoding\", \"\") or \"\").strip() or None\n                result = await aparse_text_file_to_data(file_path, silent_errors=silent_errors, encoding=encoding)\n                _raise_if_file_tool_cancelled(cancel_event)\n                if result is not None and (selected := result.data.get(\"encoding\")):\n                    selection = \"explicit\" if encoding else \"automatic\"\n                    self.log(\n                        f\"Read {Path(file_path).name} with text encoding '{selected}' ({selection}). \"\n                        \"If text looks incorrect, set Text Encoding to the file's encoding.\"\n                    )\n            except _FileToolCancelledError:\n                raise\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            else:\n                return result\n\n        docling_compatible = all(self._is_docling_compatible(str(f.path)) for f in file_list)\n\n        # Advanced path: Check if ALL files are compatible with Docling\n        if self.advanced_mode and docling_compatible:\n            final_return: list[BaseFileComponent.BaseFile] = []\n            for file in file_list:\n                _raise_if_file_tool_cancelled(cancel_event)\n                file_path = (\n                    file.path.as_posix()\n                    if settings.storage_type == \"s3\" and not file.path.is_absolute()\n                    else str(file.path)\n                )\n                advanced_data: Data | None = await self._aprocess_docling_in_subprocess(\n                    file_path, is_local_temp_file=file.cleanup_local_file\n                )\n                _raise_if_file_tool_cancelled(cancel_event)\n\n                # Expanding a large Docling export into rows is O(rows), so it stays off the event loop.\n                final_return.extend(\n                    await asyncio.to_thread(self._docling_result_to_files, file, file_path, advanced_data)\n                )\n            return final_return\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = max(1, self.concurrency_multithreading)\n\n        file_paths = [\n            f.path.as_posix() if settings.storage_type == \"s3\" and not f.path.is_absolute() else str(f.path)\n            for f in file_list\n        ]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = await aparallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        _raise_if_file_tool_cancelled(cancel_event)\n        return await asyncio.to_thread(self.rollup_data, file_list, my_data)\n\n    def _docling_result_to_files(\n        self, file: BaseFileComponent.BaseFile, file_path: str, advanced_data: Data | None\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Map one file's Docling result onto BaseFile rows (one Data row per ``doc`` element).\"\"\"\n        # Handle None case - Docling processing failed or returned None\n        if advanced_data is None:\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": \"Docling processing returned no result. Check logs for details.\",\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        # --- UNNEST: expand each element in `doc` to its own Data row\n        payload = getattr(advanced_data, \"data\", {}) or {}\n\n        # Check for errors first\n        if \"error\" in payload:\n            error_msg = payload.get(\"error\", \"Unknown error\")\n            error_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"error\": error_msg,\n                    **{k: v for k, v in payload.items() if k not in (\"error\", \"file_path\")},\n                },\n            )\n            return self.rollup_data([file], [error_data])\n\n        doc_rows = payload.get(\"doc\")\n        if isinstance(doc_rows, list) and doc_rows:\n            # Non-empty list of structured rows\n            rows: list[Data | None] = [\n                Data(\n                    data={\n                        \"file_path\": file_path,\n                        **(item if isinstance(item, dict) else {\"value\": item}),\n                    },\n                )\n                for item in doc_rows\n            ]\n            return self.rollup_data([file], rows)\n        if isinstance(doc_rows, list):\n            # Empty list - file was processed but no text content found\n            # Create a Data object indicating no content was extracted\n            self.log(f\"No text extracted from '{file_path}', creating placeholder data\")\n            empty_data = Data(\n                data={\n                    \"file_path\": file_path,\n                    \"text\": \"(No text content extracted from image)\",\n                    \"info\": \"Image processed successfully but contained no extractable text\",\n                    **{k: v for k, v in payload.items() if k != \"doc\"},\n                },\n            )\n            return self.rollup_data([file], [empty_data])\n\n        # If not structured, keep as-is (e.g., markdown export or error dict)\n        # Ensure file_path is set for proper rollup matching\n        if not payload.get(\"file_path\"):\n            payload[\"file_path\"] = file_path\n            # Create new Data with file_path\n            advanced_data = Data(\n                data=payload,\n                text=getattr(advanced_data, \"text\", None),\n            )\n        return self.rollup_data([file], [advanced_data])\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    @delegates_to(\"aload_files_helper\")\n    def load_files_helper(self) -> DataFrame:\n        return self._run_sync_loader(self.aload_files_helper)\n\n    async def aload_files_helper(self) -> DataFrame:\n        result = await self._adispatch(\"load_files\")\n\n        # Result is a DataFrame - check if it has any rows\n        if result.empty:\n            msg = \"Could not extract content from the provided file(s).\"\n            raise ValueError(msg)\n\n        # Check for error column with error messages\n        if \"error\" in result.columns:\n            errors = result[\"error\"].dropna().tolist()\n            if errors and not any(col in result.columns for col in [\"text\", \"doc\", \"exported_content\"]):\n                raise ValueError(errors[0])\n\n        return result\n\n    @delegates_to(\"aload_files_dataframe\")\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        return self._run_sync_loader(self.aload_files_dataframe)\n\n    async def aload_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return await self._adispatch(\"load_files_helper\")\n\n    @delegates_to(\"aload_files_markdown\")\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        return self._run_sync_loader(self.aload_files_markdown)\n\n    async def aload_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = await self._adispatch(\"load_files_helper\")\n\n        # Result is a DataFrame - check for text or exported_content columns\n        if \"text\" in result.columns and not result[\"text\"].isna().all():\n            text_values = result[\"text\"].dropna().tolist()\n            if text_values:\n                return Message(text=str(text_values[0]))\n\n        if \"exported_content\" in result.columns and not result[\"exported_content\"].isna().all():\n            content_values = result[\"exported_content\"].dropna().tolist()\n            if content_values:\n                return Message(text=str(content_values[0]))\n\n        # Return empty message with info that no text was found\n        return Message(text=\"(No text content extracted from file)\")\n"
             },
             "concurrency_multithreading": {
               "_input_type": "IntInput",
@@ -19445,7 +19445,7 @@
               "dependencies": [
                 {
                   "name": "aiohttp",
-                  "version": "3.14.3"
+                  "version": "3.14.5"
                 },
                 {
                   "name": "lfx",
@@ -24390,7 +24390,7 @@
                 },
                 {
                   "name": "jq",
-                  "version": null
+                  "version": "1.12.0"
                 }
               ],
               "total_dependencies": 3
@@ -25931,7 +25931,7 @@
                 },
                 {
                   "name": "jq",
-                  "version": null
+                  "version": "1.12.0"
                 }
               ],
               "total_dependencies": 4
@@ -27656,7 +27656,7 @@
                 },
                 {
                   "name": "jq",
-                  "version": null
+                  "version": "1.12.0"
                 }
               ],
               "total_dependencies": 3
@@ -32945,6 +32945,6 @@
     "num_components": 131,
     "num_modules": 18
   },
-  "sha256": "e6b403f96c220dfd4be082035187d08c8a81f4ed1b079254a57b04cb42127f2a",
+  "sha256": "047761ff7eee959154ac5dca32b4389f6e705922cca420925933ac92399d7a1e",
   "version": "1.13.0"
 }
diff --git a/uv.lock b/uv.lock
index d67f8f885e..1704fc876a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -65,12 +65,22 @@ constraints = [
     { name = "langgraph-sdk", specifier = ">=0.4.6" },
     { name = "multidict", specifier = ">=6.9.1" },
     { name = "oauthlib", specifier = ">=4.0.0,<5.0.0" },
-    { name = "pymongo", specifier = ">=4.18.2,<5.0.0" },
+    { name = "pymongo", specifier = ">=4.18.3,<5.0.0" },
     { name = "tornado", specifier = ">=6.5.10,<7.0.0" },
 ]
 overrides = [
-    { name = "authlib", specifier = ">=1.8.0,<2.0.0" },
+    { name = "aiohttp", specifier = ">=3.14.4,<4.0.0" },
+    { name = "authlib", specifier = ">=1.9.1,<2.0.0" },
+    { name = "black", specifier = ">=26.10.1,<27.0.0" },
+    { name = "brotli", marker = "platform_python_implementation == 'CPython'", specifier = ">=1.2.1,<2.0.0" },
+    { name = "brotlicffi", marker = "platform_python_implementation != 'CPython'", specifier = ">=1.2.1.0,<2.0.0" },
+    { name = "certifi", specifier = ">=2026.10.19" },
     { name = "cryptography", specifier = ">=50.0.0" },
+    { name = "datamodel-code-generator", specifier = ">=0.81.0,<0.87.0" },
+    { name = "dnspython", marker = "python_full_version < '3.11'", specifier = ">=2.8.0,<2.9.0" },
+    { name = "dnspython", marker = "python_full_version >= '3.11'", specifier = ">=2.9.0,<3.0.0" },
+    { name = "filelock", marker = "python_full_version < '3.11'", specifier = ">=3.20.3,<3.20.4" },
+    { name = "filelock", marker = "python_full_version >= '3.11'", specifier = ">=4.2.1,<5.0.0" },
     { name = "gitpython", specifier = ">=3.1.58" },
     { name = "gunicorn", specifier = ">=25.3.0" },
     { name = "h2", specifier = ">=4.4.1" },
@@ -79,12 +89,14 @@ overrides = [
     { name = "lxml", specifier = ">=6.1.0,<7.0.0" },
     { name = "mako", specifier = ">=1.3.12,<2.0.0" },
     { name = "markdown", specifier = ">=3.8.0" },
+    { name = "msgpack", specifier = ">=1.2.3,<2.0.0" },
     { name = "nltk", specifier = ">=3.10.3" },
     { name = "orjson", specifier = ">=3.11.6" },
     { name = "playwright", specifier = ">=1.59.0" },
     { name = "pyjwt", specifier = ">=2.15.1,<3.0.0" },
-    { name = "pypdf", specifier = ">=6.19.0,<7.0.0" },
+    { name = "pypdf", specifier = ">=6.21.2,<7.0.0" },
     { name = "python-liquid", specifier = ">=2.2.0,<3.0.0" },
+    { name = "setuptools", specifier = ">=83.0.0,<84.0.0" },
     { name = "urllib3", specifier = ">=2.8.0,<3.0.0" },
     { name = "virtualenv", specifier = ">=21.14.2,<22.0.0" },
     { name = "werkzeug", specifier = ">=3.1.9,<4.0.0" },
@@ -112,8 +124,8 @@ wheels = [
 
 [package.optional-dependencies]
 http-server = [
-    { name = "sse-starlette" },
-    { name = "starlette" },
+    { name = "sse-starlette", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" },
+    { name = "starlette", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" },
 ]
 
 [[package]]
@@ -121,7 +133,7 @@ name = "a2wsgi"
 version = "1.10.10"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11' and sys_platform != 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9a/cb/822c56fbea97e9eee201a2e434a80437f6750ebcb1ed307ee3a0a7505b14/a2wsgi-1.10.10.tar.gz", hash = "sha256:a5bcffb52081ba39df0d5e9a884fc6f819d92e3a42389343ba77cbf809fe1f45", size = 18799, upload-time = "2025-06-18T09:00:10.843Z" }
 wheels = [
@@ -211,7 +223,7 @@ resolution-markers = [
     "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "caio" },
+    { name = "caio", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/67/e2/d7cb819de8df6b5c1968a2756c3cb4122d4fa2b8fc768b53b7c9e5edb646/aiofile-3.9.0.tar.gz", hash = "sha256:e5ad718bb148b265b6df1b3752c4d1d83024b93da9bd599df74b9d9ffcf7919b", size = 17943, upload-time = "2024-10-08T10:39:35.846Z" }
 wheels = [
@@ -240,7 +252,7 @@ resolution-markers = [
     "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "caio" },
+    { name = "caio", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/48/41/2fea7e193e061ce54eacc3b7bc0e6a99e4fcff43c78cf0a76dd781ed8334/aiofile-3.11.1.tar.gz", hash = "sha256:1f91912c6643d2a4e49ca4ae3514f0bf3867ce948a36d99a6411b8f4755f4cf9", size = 19342, upload-time = "2026-05-16T08:18:33.538Z" }
 wheels = [
@@ -267,7 +279,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.14.3"
+version = "3.14.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -280,126 +292,127 @@ dependencies = [
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/58/d9/22ce5786ac0c1653ae8b6c23bded02c1686d11f0dbb45b31ce128e0df985/aiohttp-3.14.3.tar.gz", hash = "sha256:9491196535a88924a60afd5b5f434b5b203b6cc616250878dbdb223a8f7844bc", size = 7971213, upload-time = "2026-07-23T01:57:27.037Z" }
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-    { url = "https://files.pythonhosted.org/packages/74/e8/43b85dc55b8e950dc644babe762add781319ea881b57b33d2cce12017d12/aiohttp-3.14.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c3c200cf9757edd785051dc699c7ecbec22110dbfcb3fefc7a9f9695eda8ea7a", size = 517476, upload-time = "2026-07-23T01:52:10.846Z" },
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 version = "2.9.1"
 source = { registry = "https://pypi.org/simple" }
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 version = "2.100.0"
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+[package.optional-dependencies]
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+    { name = "rich", marker = "python_full_version >= '3.11'" },
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+    { name = "rtree", marker = "python_full_version >= '3.11'" },
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+    { name = "polyfactory", marker = "python_full_version >= '3.11'" },
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 ]
 feat-ocr-rapidocr = [
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 ]
 format-latex = [
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+    { name = "pylatexenc", marker = "python_full_version >= '3.11'" },
 ]
 format-office = [
-    { name = "openpyxl" },
-    { name = "python-docx" },
-    { name = "python-pptx" },
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+    { name = "pylatexenc", marker = "python_full_version >= '3.11'" },
+    { name = "python-docx", marker = "python_full_version >= '3.11'" },
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 ]
 format-pdf = [
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 ]
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-    { name = "marko" },
+    { name = "beautifulsoup4", marker = "python_full_version >= '3.11'" },
+    { name = "marko", marker = "python_full_version >= '3.11'" },
 ]
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+    { name = "huggingface-hub", marker = "python_full_version >= '3.11'" },
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-    { name = "python-dotenv" },
-    { name = "rich" },
-    { name = "typer" },
-    { name = "websockets" },
+    { name = "httpx", marker = "python_full_version >= '3.11'" },
+    { name = "python-dotenv", marker = "python_full_version >= '3.11'" },
+    { name = "rich", marker = "python_full_version >= '3.11'" },
+    { name = "typer", marker = "python_full_version >= '3.11'" },
+    { name = "websockets", marker = "python_full_version >= '3.11'" },
 ]
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-    { name = "defusedxml" },
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+    { name = "accelerate", marker = "python_full_version >= '3.11'" },
+    { name = "beautifulsoup4", marker = "python_full_version >= '3.11'" },
+    { name = "defusedxml", marker = "python_full_version >= '3.11'" },
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+    { name = "docling-ibm-models", version = "4.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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+    { name = "httpx", marker = "python_full_version >= '3.11'" },
+    { name = "huggingface-hub", marker = "python_full_version >= '3.11'" },
+    { name = "mail-parser", marker = "python_full_version >= '3.11'" },
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+    { name = "polyfactory", marker = "python_full_version >= '3.11'" },
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+    { name = "python-pptx", marker = "python_full_version >= '3.11'" },
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+    { name = "typer", marker = "python_full_version >= '3.11'" },
+    { name = "websockets", marker = "python_full_version >= '3.11'" },
 ]
 
 [[package]]
@@ -3713,7 +4152,8 @@ dependencies = [
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     { name = "opencv-python-headless" },
-    { name = "pillow" },
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+    { name = "pillow", version = "13.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pyclipper" },
     { name = "python-bidi" },
     { name = "pyyaml" },
@@ -3791,12 +4231,12 @@ resolution-markers = [
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 ]
 dependencies = [
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-    { name = "pydantic-core" },
-    { name = "requests" },
-    { name = "typing-extensions" },
-    { name = "websockets" },
+    { name = "httpx", marker = "python_full_version == '3.12.*'" },
+    { name = "pydantic", marker = "python_full_version == '3.12.*'" },
+    { name = "pydantic-core", marker = "python_full_version == '3.12.*'" },
+    { name = "requests", marker = "python_full_version == '3.12.*'" },
+    { name = "typing-extensions", marker = "python_full_version == '3.12.*'" },
+    { name = "websockets", marker = "python_full_version == '3.12.*'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/db/83/fd165b38a69a4a40746926a908ea92e456a0e0dd5b6038836c9cc94a3487/elevenlabs-1.58.1.tar.gz", hash = "sha256:e9f723a528c1bbd80605e639e858f7a58f204860faa9417305a4083508c7c0fb", size = 185830, upload-time = "2025-05-07T13:54:37.814Z" }
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@@ -3825,12 +4265,12 @@ resolution-markers = [
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 dependencies = [
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-    { name = "pydantic-core" },
-    { name = "requests" },
-    { name = "typing-extensions" },
-    { name = "websockets" },
+    { name = "httpx", marker = "python_full_version != '3.12.*'" },
+    { name = "pydantic", marker = "python_full_version != '3.12.*'" },
+    { name = "pydantic-core", marker = "python_full_version != '3.12.*'" },
+    { name = "requests", marker = "python_full_version != '3.12.*'" },
+    { name = "typing-extensions", marker = "python_full_version != '3.12.*'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/73/5f/01197145be5be258abdce254010eb300868b85fbf6cf1c6c1538a68caef4/elevenlabs-1.59.0.tar.gz", hash = "sha256:16e735bd594e86d415dd445d249c8cc28b09996cfd627fbc10102c0a84698859", size = 200549, upload-time = "2025-05-15T12:19:28.868Z" }
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@@ -3842,7 +4282,8 @@ name = "email-validator"
 version = "2.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "dnspython" },
+    { name = "dnspython", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "dnspython", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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@@ -3911,16 +4352,16 @@ name = "exec-sandbox"
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+    { name = "click", marker = "python_full_version >= '3.12'" },
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+    { name = "pydantic", marker = "python_full_version >= '3.12'" },
+    { name = "pydantic-settings", marker = "python_full_version >= '3.12'" },
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-    { name = "boltons" },
+    { name = "boltons", marker = "python_full_version >= '3.12'" },
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@@ -3980,9 +4421,9 @@ resolution-markers = [
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-    { name = "pydantic-extra-types" },
-    { name = "pydantic-settings" },
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+    { name = "fastar", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" },
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-    { name = "typer" },
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 dependencies = [
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+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' and python_full_version < '3.14'" },
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-    { name = "packaging" },
-    { name = "pyarrow" },
-    { name = "pydantic" },
-    { name = "tqdm" },
+    { name = "packaging", marker = "python_full_version >= '3.12'" },
+    { name = "pyarrow", marker = "python_full_version >= '3.12'" },
+    { name = "pydantic", marker = "python_full_version >= '3.12'" },
+    { name = "tqdm", marker = "python_full_version >= '3.12'" },
 ]
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@@ -7087,7 +7598,7 @@ wheels = [
 
 [package.optional-dependencies]
 valkey = [
-    { name = "valkey-glide-sync" },
+    { name = "valkey-glide-sync", marker = "sys_platform != 'win32'" },
 ]
 
 [[package]]
@@ -7216,7 +7727,8 @@ name = "langchain-docling"
 version = "2.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "docling" },
+    { name = "docling", version = "2.132.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "docling", version = "2.142.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "langchain-core" },
 ]
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     { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "pymongo" },
+    { name = "pymongo", version = "4.18.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pymongo", version = "4.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
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@@ -7544,14 +8057,14 @@ name = "langchain-pinecone"
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-    { name = "langchain-core" },
-    { name = "langchain-openai" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or python_full_version >= '3.14'" },
+    { name = "httpx", marker = "python_full_version < '3.14'" },
+    { name = "langchain-core", marker = "python_full_version < '3.14'" },
+    { name = "langchain-openai", marker = "python_full_version < '3.14'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
-    { name = "pinecone", extra = ["asyncio"] },
-    { name = "pydantic" },
-    { name = "simsimd" },
+    { name = "pinecone", extra = ["asyncio"], marker = "python_full_version < '3.14'" },
+    { name = "pydantic", marker = "python_full_version < '3.14'" },
+    { name = "simsimd", marker = "python_full_version < '3.14'" },
 ]
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@@ -7926,7 +8439,8 @@ dependencies = [
     { name = "emoji" },
     { name = "fastapi" },
     { name = "fastapi-pagination" },
-    { name = "filelock" },
+    { name = "filelock", version = "3.20.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "filelock", version = "4.2.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "grandalf" },
     { name = "greenlet" },
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@@ -7978,7 +8492,8 @@ dependencies = [
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-    { name = "pillow" },
+    { name = "pillow", version = "12.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pillow", version = "13.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pip" },
     { name = "platformdirs" },
     { name = "prometheus-client" },
@@ -7986,7 +8501,8 @@ dependencies = [
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     { name = "pydantic-settings" },
     { name = "pyjwt" },
-    { name = "pymongo" },
+    { name = "pymongo", version = "4.18.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pymongo", version = "4.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pypdf" },
     { name = "pyperclip" },
     { name = "python-docx" },
@@ -8081,23 +8597,31 @@ datasets = [
     { name = "datasets" },
 ]
 docling = [
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-    { name = "docling-slim", extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" },
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+    { name = "docling-core", version = "2.104.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "docling-slim", version = "2.132.0", source = { registry = "https://pypi.org/simple" }, extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "(python_full_version < '3.11' and platform_machine != 'x86_64') or (python_full_version < '3.11' and sys_platform != 'darwin')" },
+    { name = "docling-slim", version = "2.142.1", source = { registry = "https://pypi.org/simple" }, extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "(python_full_version >= '3.11' and platform_machine != 'x86_64') or (python_full_version >= '3.11' and sys_platform != 'darwin')" },
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+    { name = "docling-core", version = "2.104.0", source = { registry = "https://pypi.org/simple" }, extra = ["chunking"], marker = "python_full_version >= '3.11'" },
+    { name = "docling-slim", version = "2.132.0", source = { registry = "https://pypi.org/simple" }, extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "(python_full_version < '3.11' and platform_machine != 'x86_64') or (python_full_version < '3.11' and sys_platform != 'darwin')" },
+    { name = "docling-slim", version = "2.142.1", source = { registry = "https://pypi.org/simple" }, extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "(python_full_version >= '3.11' and platform_machine != 'x86_64') or (python_full_version >= '3.11' and sys_platform != 'darwin')" },
     { name = "tiktoken" },
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 docling-image-description = [
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-    { name = "docling-slim", extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" },
+    { name = "docling-core", version = "2.100.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "docling-core", version = "2.104.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "docling-slim", version = "2.132.0", source = { registry = "https://pypi.org/simple" }, extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "(python_full_version < '3.11' and platform_machine != 'x86_64') or (python_full_version < '3.11' and sys_platform != 'darwin')" },
+    { name = "docling-slim", version = "2.142.1", source = { registry = "https://pypi.org/simple" }, extra = ["cli", "convert-core", "extract-core", "feat-ocr-rapidocr", "format-latex", "format-office", "format-pdf", "format-web", "models-local", "service-client"], marker = "(python_full_version >= '3.11' and platform_machine != 'x86_64') or (python_full_version >= '3.11' and sys_platform != 'darwin')" },
     { name = "langchain-docling" },
 ]
 docx = [
     { name = "python-docx" },
 ]
 duckduckgo = [
+    { name = "brotli", marker = "platform_python_implementation == 'CPython'" },
+    { name = "brotlicffi", marker = "platform_python_implementation != 'CPython'" },
     { name = "ddgs" },
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 easyocr = [
@@ -8220,7 +8744,8 @@ mlx = [
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 ]
 mongodb = [
-    { name = "pymongo" },
+    { name = "pymongo", version = "4.18.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pymongo", version = "4.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 mongodb-vectors = [
     { name = "langchain-mongodb" },
@@ -8399,6 +8924,8 @@ requires-dist = [
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     { name = "boto3", marker = "extra == 'aws'", specifier = ">=1.34.162,<2.0.0" },
+    { name = "brotli", marker = "platform_python_implementation == 'CPython' and extra == 'duckduckgo'", specifier = ">=1.2.1,<2.0.0" },
+    { name = "brotlicffi", marker = "platform_python_implementation != 'CPython' and extra == 'duckduckgo'", specifier = ">=1.2.1.0,<2.0.0" },
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@@ -8440,7 +8967,8 @@ requires-dist = [
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@@ -8551,7 +9079,8 @@ requires-dist = [
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     { name = "pgvector", marker = "extra == 'pgvector'", specifier = ">=0.4.2" },
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+    { name = "pillow", marker = "python_full_version < '3.11'", specifier = ">=12.3.0,<13.0.0" },
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@@ -8564,8 +9093,8 @@ requires-dist = [
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-    { name = "pypdf", marker = "extra == 'pypdf'", specifier = ">=6.19.0,<7.0.0" },
+    { name = "pypdf", specifier = ">=6.21.2,<7.0.0" },
+    { name = "pypdf", marker = "extra == 'pypdf'", specifier = ">=6.21.2,<7.0.0" },
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-    { name = "pydantic" },
-    { name = "python-liquid" },
-    { name = "pyyaml" },
-    { name = "retry" },
-    { name = "termcolor" },
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+    { name = "coolname", marker = "python_full_version < '3.14'" },
+    { name = "deprecated", marker = "python_full_version < '3.14'" },
+    { name = "httpx", marker = "python_full_version < '3.14'" },
+    { name = "langchain-core", marker = "python_full_version < '3.14'" },
+    { name = "nanoid", marker = "python_full_version < '3.14'" },
+    { name = "openinference-instrumentation-haystack", marker = "python_full_version < '3.14'" },
+    { name = "openinference-instrumentation-langchain", marker = "python_full_version < '3.14'" },
+    { name = "openinference-instrumentation-openai", marker = "python_full_version < '3.14'" },
+    { name = "openinference-semantic-conventions", marker = "python_full_version < '3.14'" },
+    { name = "opentelemetry-api", marker = "python_full_version < '3.14'" },
+    { name = "opentelemetry-exporter-otlp-proto-http", marker = "python_full_version < '3.14'" },
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+    { name = "opentelemetry-sdk", marker = "python_full_version < '3.14'" },
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+    { name = "python-liquid", marker = "python_full_version < '3.14'" },
+    { name = "pyyaml", marker = "python_full_version < '3.14'" },
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@@ -8945,7 +9474,8 @@ dependencies = [
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     { name = "emoji" },
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-    { name = "filelock" },
+    { name = "filelock", version = "3.20.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "filelock", version = "4.2.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "gunicorn" },
     { name = "h2" },
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@@ -8966,7 +9496,8 @@ dependencies = [
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-    { name = "pillow" },
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     { name = "platformdirs" },
     { name = "psutil" },
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@@ -9059,7 +9590,8 @@ requires-dist = [
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-    { name = "filelock", specifier = ">=3.20.1,<4.0.0" },
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+    { name = "pillow", marker = "python_full_version < '3.11'", specifier = ">=12.3.0,<13.0.0" },
+    { name = "pillow", marker = "python_full_version >= '3.11'", specifier = ">=13.0.0,<14.0.0" },
     { name = "platformdirs", specifier = ">=4.3.8,<5.0.0" },
     { name = "psutil", specifier = ">=5.9.0,<8.0.0" },
     { name = "pydantic", specifier = ">=2.0.0,<2.14" },
     { name = "pydantic-settings", specifier = ">=2.10.1,<3.0.0" },
     { name = "pyjwt", specifier = ">=2.15.1,<3.0.0" },
-    { name = "pypdf", specifier = ">=6.19.0,<7.0.0" },
+    { name = "pypdf", specifier = ">=6.21.2,<7.0.0" },
     { name = "python-dotenv", specifier = ">=1.0.0,<2.0.0" },
     { name = "pyyaml", specifier = ">=6.0.0,<7.0.0" },
     { name = "referencing", specifier = ">=0.31.0,<1.0.0" },
@@ -9228,7 +9761,8 @@ all = [
     { name = "composio-langchain" },
     { name = "couchbase" },
     { name = "cuga", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" },
-    { name = "dnspython" },
+    { name = "dnspython", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "dnspython", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "elasticsearch" },
     { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
     { name = "faiss-cpu", version = "1.14.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
@@ -9265,7 +9799,8 @@ all = [
     { name = "openai" },
     { name = "opensearch-py" },
     { name = "pgvector" },
-    { name = "pymongo" },
+    { name = "pymongo", version = "4.18.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pymongo", version = "4.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pytube" },
     { name = "qdrant-client" },
     { name = "qianfan" },
@@ -9296,7 +9831,8 @@ all-no-torch = [
     { name = "composio" },
     { name = "composio-langchain" },
     { name = "couchbase" },
-    { name = "dnspython" },
+    { name = "dnspython", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "dnspython", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "elasticsearch" },
     { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
     { name = "faiss-cpu", version = "1.14.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
@@ -9331,7 +9867,8 @@ all-no-torch = [
     { name = "openai" },
     { name = "opensearch-py" },
     { name = "pgvector" },
-    { name = "pymongo" },
+    { name = "pymongo", version = "4.18.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pymongo", version = "4.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pytube" },
     { name = "qdrant-client" },
     { name = "qianfan" },
@@ -9395,7 +9932,8 @@ confluence = [
 ]
 couchbase = [
     { name = "couchbase" },
-    { name = "dnspython" },
+    { name = "dnspython", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "dnspython", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "langchain-community" },
 ]
 cuga = [
@@ -9465,7 +10003,8 @@ mistral = [
 ]
 mongodb = [
     { name = "langchain-mongodb" },
-    { name = "pymongo" },
+    { name = "pymongo", version = "4.18.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pymongo", version = "4.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 mrscraper = [
     { name = "mrscraper-sdk" },
@@ -9903,15 +10442,18 @@ name = "lfx-docling"
 version = "0.2.1"
 source = { editable = "src/bundles/docling" }
 dependencies = [
-    { name = "docling-core" },
+    { name = "docling-core", version = "2.100.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "docling-core", version = "2.104.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "httpx" },
     { name = "lfx" },
 ]
 
 [package.optional-dependencies]
 all = [
-    { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" },
-    { name = "docling-core", extra = ["chunking"] },
+    { name = "docling", version = "2.132.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and platform_machine != 'x86_64') or (python_full_version < '3.11' and sys_platform != 'darwin')" },
+    { name = "docling", version = "2.142.1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and platform_machine != 'x86_64') or (python_full_version >= '3.11' and sys_platform != 'darwin')" },
+    { name = "docling-core", version = "2.100.0", source = { registry = "https://pypi.org/simple" }, extra = ["chunking"], marker = "python_full_version < '3.11'" },
+    { name = "docling-core", version = "2.104.0", source = { registry = "https://pypi.org/simple" }, extra = ["chunking"], marker = "python_full_version >= '3.11'" },
     { name = "langchain-docling" },
     { name = "ocrmac", marker = "sys_platform == 'darwin'" },
     { name = "rapidocr-onnxruntime" },
@@ -9924,12 +10466,14 @@ all = [
     { name = "transformers" },
 ]
 chunking = [
-    { name = "docling-core", extra = ["chunking"] },
+    { name = "docling-core", version = "2.100.0", source = { registry = "https://pypi.org/simple" }, extra = ["chunking"], marker = "python_full_version < '3.11'" },
+    { name = "docling-core", version = "2.104.0", source = { registry = "https://pypi.org/simple" }, extra = ["chunking"], marker = "python_full_version >= '3.11'" },
     { name = "tiktoken" },
     { name = "transformers" },
 ]
 image-description = [
-    { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" },
+    { name = "docling", version = "2.132.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and platform_machine != 'x86_64') or (python_full_version < '3.11' and sys_platform != 'darwin')" },
+    { name = "docling", version = "2.142.1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and platform_machine != 'x86_64') or (python_full_version >= '3.11' and sys_platform != 'darwin')" },
     { name = "langchain-docling" },
     { name = "ocrmac", marker = "sys_platform == 'darwin'" },
     { name = "rapidocr-onnxruntime" },
@@ -9941,7 +10485,8 @@ image-description = [
     { name = "transformers" },
 ]
 local = [
-    { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" },
+    { name = "docling", version = "2.132.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and platform_machine != 'x86_64') or (python_full_version < '3.11' and sys_platform != 'darwin')" },
+    { name = "docling", version = "2.142.1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and platform_machine != 'x86_64') or (python_full_version >= '3.11' and sys_platform != 'darwin')" },
     { name = "ocrmac", marker = "sys_platform == 'darwin'" },
     { name = "rapidocr-onnxruntime" },
     { name = "tesserocr" },
@@ -9975,9 +10520,11 @@ provides-extras = ["local", "chunking", "image-description", "all"]
 
 [[package]]
 name = "lfx-duckduckgo"
-version = "0.2.0"
+version = "0.2.1"
 source = { editable = "src/bundles/duckduckgo" }
 dependencies = [
+    { name = "brotli", marker = "platform_python_implementation == 'CPython'" },
+    { name = "brotlicffi", marker = "platform_python_implementation != 'CPython'" },
     { name = "ddgs" },
     { name = "langchain-community" },
     { name = "lfx" },
@@ -9985,6 +10532,8 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
+    { name = "brotli", marker = "platform_python_implementation == 'CPython'", specifier = ">=1.2.1,<2.0.0" },
+    { name = "brotlicffi", marker = "platform_python_implementation != 'CPython'", specifier = ">=1.2.1.0,<2.0.0" },
     { name = "ddgs", specifier = ">=9.0.0" },
     { name = "langchain-community", specifier = ">=0.4.1,<1.0.0" },
     { name = "lfx", editable = "src/lfx" },
@@ -10121,13 +10670,14 @@ requires-dist = [{ name = "lfx", editable = "src/lfx" }]
 
 [[package]]
 name = "lfx-nextplaid"
-version = "0.2.0"
+version = "0.2.1"
 source = { editable = "src/bundles/nextplaid" }
 dependencies = [
     { name = "httpx" },
     { name = "langchain-plaid" },
     { name = "lfx" },
-    { name = "pillow" },
+    { name = "pillow", version = "12.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pillow", version = "13.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 
 [package.metadata]
@@ -10135,7 +10685,8 @@ requires-dist = [
     { name = "httpx", specifier = ">=0.28.1,<1.0.0" },
     { name = "langchain-plaid", specifier = ">=0.1.0,<1.0.0" },
     { name = "lfx", editable = "src/lfx" },
-    { name = "pillow", specifier = ">=12.3.0,<13.0.0" },
+    { name = "pillow", marker = "python_full_version < '3.11'", specifier = ">=12.3.0,<13.0.0" },
+    { name = "pillow", marker = "python_full_version >= '3.11'", specifier = ">=13.0.0,<14.0.0" },
 ]
 
 [[package]]
@@ -10829,13 +11380,13 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "click" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "onnxruntime", version = "1.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "onnxruntime", version = "1.28.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "python-dotenv" },
+    { name = "click", marker = "sys_platform == 'win32'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' and sys_platform == 'win32'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'" },
+    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14' and sys_platform == 'win32'" },
+    { name = "onnxruntime", version = "1.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14' and sys_platform == 'win32'" },
+    { name = "onnxruntime", version = "1.28.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14' and sys_platform == 'win32'" },
+    { name = "python-dotenv", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/fe/b6/8fdd991142ad3e037179a494b153f463024e5a211ef3ad948b955c26b4de/magika-0.6.2.tar.gz", hash = "sha256:37eb6ae8020f6e68f231bc06052c0a0cbe8e6fa27492db345e8dc867dbceb067", size = 3036634, upload-time = "2025-05-02T14:54:18.88Z" }
 wheels = [
@@ -10864,13 +11415,13 @@ resolution-markers = [
     "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "click" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "onnxruntime", version = "1.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "onnxruntime", version = "1.28.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "python-dotenv" },
+    { name = "click", marker = "sys_platform != 'win32'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' and sys_platform != 'win32'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform != 'win32'" },
+    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14' and sys_platform != 'win32'" },
+    { name = "onnxruntime", version = "1.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14' and sys_platform != 'win32'" },
+    { name = "onnxruntime", version = "1.28.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14' and sys_platform != 'win32'" },
+    { name = "python-dotenv", marker = "sys_platform != 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a3/f3/3d1dcdd7b9c41d589f5cff252d32ed91cdf86ba84391cfc81d9d8773571d/magika-0.6.3.tar.gz", hash = "sha256:7cc52aa7359af861957043e2bf7265ed4741067251c104532765cd668c0c0cb1", size = 3042784, upload-time = "2025-10-30T15:22:34.499Z" }
 wheels = [
@@ -11058,8 +11609,8 @@ wheels = [
 
 [package.optional-dependencies]
 cli = [
-    { name = "python-dotenv" },
-    { name = "typer" },
+    { name = "python-dotenv", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" },
+    { name = "typer", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" },
 ]
 
 [[package]]
@@ -11126,12 +11677,12 @@ name = "milvus-lite"
 version = "3.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" } },
-    { name = "grpcio" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or python_full_version >= '3.14'" },
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
-    { name = "pyarrow" },
-    { name = "tomli", marker = "python_full_version < '3.11' or python_full_version >= '3.14'" },
+    { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.14' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'win32')" },
+    { name = "grpcio", marker = "(python_full_version < '3.14' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'win32')" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32')" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and python_full_version < '3.14' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'win32')" },
+    { name = "pyarrow", marker = "(python_full_version < '3.14' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'win32')" },
+    { name = "tomli", marker = "(python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/d1/d0/3edb31f832287557c8a8bb0515aec2c73265512040dcd4fc43d747722d11/milvus_lite-3.1.1.tar.gz", hash = "sha256:0cbf8386a2e99b6464a5eda14cf8e31934b0e287e36c831e293aee614d9efc2e", size = 647172, upload-time = "2026-07-27T05:03:56.834Z" }
 wheels = [
@@ -11143,7 +11694,7 @@ name = "ml-dtypes"
 version = "0.5.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' and python_full_version < '3.14'" },
     { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0e/4a/c27b42ed9b1c7d13d9ba8b6905dece787d6259152f2309338aed29b2447b/ml_dtypes-0.5.4.tar.gz", hash = "sha256:8ab06a50fb9bf9666dd0fe5dfb4676fa2b0ac0f31ecff72a6c3af8e22c063453", size = 692314, upload-time = "2025-11-17T22:32:31.031Z" }
@@ -11189,7 +11740,7 @@ name = "mlx"
 version = "0.32.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "mlx-metal" },
+    { name = "mlx-metal", marker = "(python_full_version >= '3.14' and platform_machine != 'arm64' and sys_platform == 'darwin') or (python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/df/38/a034159df4d21ef7b5721225a2c46092e20a2c627724f57be3e89fa7dbce/mlx-0.32.0-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:c6feb17e32160b70c7634aab925cf3f8c5c7bebbf99f227c48450478e1008af2", size = 562899, upload-time = "2026-07-07T17:55:25.157Z" },
@@ -11214,14 +11765,14 @@ name = "mlx-lm"
 version = "0.31.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jinja2" },
-    { name = "mlx" },
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "protobuf" },
-    { name = "pyyaml" },
-    { name = "sentencepiece" },
-    { name = "transformers" },
+    { name = "jinja2", marker = "(python_full_version >= '3.14' and platform_machine != 'arm64' and sys_platform == 'darwin') or (python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin')" },
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