From f91b675190bd66d348c64f2622eaafdf982b42f9 Mon Sep 17 00:00:00 2001
From: tfeltin <tfeltin@cisco.com>
Date: Mon, 26 Oct 2026 11:44:16 +0100
Subject: [PATCH 1/2] docs(scope): clean up README

Signed-off-by: tfeltin <tfeltin@cisco.com>
---
 README.md                 |  80 +++++++++++++++++++++++++-------------
 docs/assets/dashboard.png | Bin 0 -> 498744 bytes
 2 files changed, 54 insertions(+), 26 deletions(-)
 create mode 100644 docs/assets/dashboard.png

diff --git a/README.md b/README.md
index 9ad5e50..002f7f5 100644
--- a/README.md
+++ b/README.md
@@ -1,39 +1,52 @@
 # System for Cognitive Observability Provenance and Explainability (SCOPE)
 
-SCOPE is a modular platform for observing, analyzing, and explaining the executions of Multi-Agent Systems (MAS) from OpenTelemetry traces. It ingests raw OTel spans, maps them onto a shared MAS ontology, and enriches each session with analysis such as semantic grouping, anomaly detection, consistency checking, and stateful trajectory evaluation — surfaced through a web dashboard.
+SCOPE observes, analyzes and explains the executions of Multi-Agent Systems (MAS) from their OpenTelemetry traces. It maps raw spans onto a shared MAS ontology, keeps every execution as a knowledge graph, and enriches each session with analyses such as semantic grouping, anomaly detection, consistency checking, cognitive-failure detection and stateful trajectory evaluation. A web dashboard and an API serve the result.
 
-## How it works
+![A session in the SCOPE dashboard: its spans and the execution timeline of the agents and tools](docs/assets/dashboard.png)
 
-1. A MAS sends OTel spans to an OTel collector, which stores them (ClickHouse by default, swappable for another backend).
-2. Once a trace is complete, the SCOPE ingestion pipeline maps the raw spans onto the [MAS ontology](ontology), producing a structured execution model.
-3. The session representation is enriched by the analysis workers (semantic grouping, anomaly detection, consistency checking, stateful evaluation, metrics).
-4. The enriched knowledge graph is served through the [API](backend/api) and visualized in the [UI](ui), giving explainability and insight into the MAS's inner workings.
+## Quick start
 
-## Repository structure
+Run the whole stack on your machine with Docker Compose, send it the trace of a small two-agent example, and look at it in the dashboard. It takes about 15 minutes, most of it the first image build.
 
-| Path | Description |
-| --- | --- |
-| [`backend/`](backend) | Python libraries and workers implementing ingestion, normalization, analysis, and the data API. See [backend/README.md](backend/README.md). |
-| [`backend/api`](backend/api) | Unified DB access layer and REST API over the raw OTel spans and the knowledge graph. |
-| [`backend/norm`](backend/norm) | Normalizes raw OTel traces into structured MAS execution models. |
-| [`backend/mce`](backend/mce) | Metric computation engine for sessions. |
-| [`backend/stateful-evals`](backend/stateful-evals) | Cost-effective evaluation of a session's trajectory. |
-| [`backend/workers`](backend/workers) | Standalone workers built on top of the backend libraries. |
-| [`ui/`](ui) | React/TypeScript web dashboard for browsing applications, sessions, execution graphs, and live topology. See [ui/README.md](docs/platform/ui/index.md). |
-| [`ontology/`](ontology) | The MAS telemetry ontology (Turtle/OWL) and the `scope-ontology` Python package. See [ontology/README.md](ontology/README.md). |
-| [`docs/`](docs) | Documentation site sources (MkDocs), built with `mkdocs.yml`. |
-| [`deploy/`](deploy) | Deployment: Helm charts for Kubernetes (`deploy/k8s`) and a Docker Compose stack (`deploy/docker-compose`) running the API, UI, workers and supporting infra. |
+You need Docker (Compose v2.17 or later, with at least **12 GB** of memory given to it) and [`uv`](https://docs.astral.sh/uv/) to run the example. An OpenAI-compatible LLM endpoint is only needed for the analyses that call a model: without it, traces are still collected and stored in the knowledge graph.
+
+### 1. Start SCOPE
+
+```bash
+git clone https://github.com/ioc-project/scope.git
+cd scope/deploy/docker-compose
+cp .env.template .env        # set LLM_MODEL_NAME, EMBEDDING_MODEL_NAME, LLM_GATEWAY_* and the change-me passwords
+docker compose up -d --build
+```
+
+### 2. Send a trace**
+
+```bash
+cd ../../examples/currency-demo
+OTLP_HTTP_ENDPOINT=http://localhost:4318 uv run main.py 100
+```
+
+```text
+100.00 USD = 85.90 EUR (example rate)
+```
 
-## Getting started
+### 3. Look at it
 
-Each component has its own setup instructions:
+| What | Where |
+|---|---|
+| Dashboard and API: *Applications* → `currency-demo` → *Inspect* | <http://localhost:8080> |
+| The knowledge graph, in the Neo4j browser (`neo4j` and your `NEO4J_PASSWORD`) | <http://localhost:7474> |
 
-- Backend (API, workers, local Docker Compose stack): [backend/README.md](backend/README.md)
-- UI (dashboard): [ui/README.md](docs/platform/ui/index.md)
-- Ontology (Python package, docs, versioning): [ontology/README.md](ontology/README.md)
-- Kubernetes deployment: [deploy/k8s/charts/scope/README.md](deploy/k8s/charts/scope/README.md)
+The session shows up a few seconds after the example exits. The [first-trace tutorial](docs/tutorials/first-trace.md) walks through each step with screenshots, and explains how to query the knowledge graph.
 
-For a fully local stack, see the [Docker Compose deployment](deploy/docker-compose/README.md), which runs the same components as the Helm chart: OTel collector, ClickHouse, RabbitMQ, Neo4j, the ingestion pipeline, the API and the UI.
+**Tear down** with `docker compose down` (add `-v` to also delete the data). To run SCOPE on Kubernetes instead, use the [Helm chart](deploy/k8s/charts/scope/README.md). To trace your own application, see [instrument your app](docs/tutorials/instrument-your-app.md).
+
+## How it works
+
+1. A MAS sends OTel spans to an OTel collector, which stores them (ClickHouse by default, swappable for another backend).
+2. Once a trace is complete, the SCOPE ingestion pipeline maps the raw spans onto the [MAS ontology](ontology), producing a structured execution model: the topology of the system, and for each session its calls, states and transitions.
+3. The session is enriched by the analysis workers (semantic grouping, anomaly detection, consistency checking, cognitive failures, stateful evaluation, metrics).
+4. The enriched knowledge graph is served through the [API](backend/api) and visualized in the [UI](ui), giving explainability and insight into the MAS's inner workings.
 
 ## Documentation
 
@@ -50,6 +63,21 @@ The ontology reference documentation (Widoco and WebVOWL) is generated separatel
 
 - **Ontology**: [ontology/index.md](ontology/index.md), also published at [ioc-project.github.io/scope/ontologies.html](https://ioc-project.github.io/scope/ontologies.html)
 
+## Repository structure
+
+| Path | Description |
+| --- | --- |
+| [`backend/`](backend) | Python libraries and workers implementing ingestion, normalization, analysis, and the data API. See [backend/README.md](backend/README.md). |
+| [`backend/api`](backend/api) | Unified DB access layer and REST API over the raw OTel spans and the knowledge graph. |
+| [`backend/norm`](backend/norm) | Normalizes raw OTel traces into structured MAS execution models. |
+| [`backend/mce`](backend/mce) | Metric computation engine for sessions. |
+| [`backend/stateful-evals`](backend/stateful-evals) | Cost-effective evaluation of a session's trajectory. |
+| [`backend/workers`](backend/workers) | Standalone workers built on top of the backend libraries. |
+| [`ui/`](ui) | React/TypeScript web dashboard for browsing applications, sessions, execution graphs, and live topology. See [ui/README.md](docs/platform/ui/index.md). |
+| [`ontology/`](ontology) | The MAS telemetry ontology (Turtle/OWL) and the `scope-ontology` Python package. See [ontology/README.md](ontology/README.md). |
+| [`docs/`](docs) | Documentation site sources (MkDocs), built with `mkdocs.yml`. |
+| [`deploy/`](deploy) | Deployment: Helm charts for Kubernetes (`deploy/k8s`) and a Docker Compose stack (`deploy/docker-compose`) running the API, UI, workers and supporting infra. |
+
 ## Contributing
 
 Contributions are welcome — see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines, [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) for community standards, and [SECURITY.md](SECURITY.md) for reporting vulnerabilities. Maintainers are listed in [MAINTAINERS.md](MAINTAINERS.md).
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HcmV?d00001


From 244910f5dd227c9b03d417e66c52424a94b83517 Mon Sep 17 00:00:00 2001
From: tfeltin <tfeltin@cisco.com>
Date: Tue, 27 Oct 2026 17:27:05 +0100
Subject: [PATCH 2/2] refactor(scope): analysis tutorial and correpsonding
 fixes

Signed-off-by: tfeltin <tfeltin@cisco.com>
---
 README.md                                     |    4 +-
 backend/CHANGELOG.md                          |   17 +
 backend/api/scope/client/dal.py               |   13 +-
 backend/api/scope/connectors/clickhouse.py    |   10 +
 backend/api/scope/connectors/neo4j.py         |   64 +-
 backend/api/scope/dependencies.py             |    8 +-
 backend/api/tests/test_clickhouse_pool.py     |  117 +
 backend/api/tests/test_failure_reports.py     |    4 +-
 .../api/tests/test_neo4j_transient_errors.py  |  192 +
 backend/api/tests/test_structural_topology.py |   35 +-
 .../mce/mce-client/src/mce/client/worker.py   |  114 +-
 .../tests/client/test_worker_concurrency.py   |   47 +
 .../norm/src/norm/ioa_observe/trajectory.py   |    9 +-
 backend/norm/tests/test_state_continuity.py   |   44 +-
 .../src/worker_base/base_worker.py            |   16 +
 backend/worker-base/tests/test_thread_pool.py |   57 +
 .../cognitive_failure_worker/trace_file.py    |    4 +-
 .../tests/test_trace_file.py                  |    4 +-
 .../src/mce_worker/wrapper/mce_wrapper.py     |   10 +-
 .../tests/test_mce_wrapper_threads.py         |   51 +
 docs/analysis/components/worker-base.md       |    4 +-
 .../tutorials/semantic-analysis/.gitkeep      |    0
 .../semantic-analysis/neo4j-groups.png        |  Bin 0 -> 600771 bytes
 .../neo4j-judge-reasoning.png                 |  Bin 0 -> 475719 bytes
 .../trip-planner-timeline.png                 |  Bin 0 -> 435226 bytes
 .../semantic-analysis/ui-group-details.png    |  Bin 0 -> 608164 bytes
 .../semantic-analysis/ui-outlier-sessions.png |  Bin 0 -> 249774 bytes
 .../semantic-analysis/ui-semantic-groups.png  |  Bin 0 -> 748153 bytes
 docs/tutorials/index.md                       |   11 +
 docs/tutorials/semantic-analysis.md           |  329 ++
 examples/trip-planner/README.md               |   47 +
 examples/trip-planner/data/network.json       |   20 +
 examples/trip-planner/load_traces.py          |  110 +
 examples/trip-planner/pyproject.toml          |   11 +
 examples/trip-planner/questions.py            |   62 +
 examples/trip-planner/record_traces.py        |   83 +
 .../traces/trip_planner_sessions.jsonl.gz     |  Bin 0 -> 378494 bytes
 examples/trip-planner/trip_planner.py         |  320 ++
 examples/trip-planner/uv.lock                 | 4087 +++++++++++++++++
 mkdocs.yml                                    |    1 +
 40 files changed, 5815 insertions(+), 90 deletions(-)
 create mode 100644 backend/api/tests/test_clickhouse_pool.py
 create mode 100644 backend/api/tests/test_neo4j_transient_errors.py
 create mode 100644 backend/mce/tests/client/test_worker_concurrency.py
 create mode 100644 backend/worker-base/tests/test_thread_pool.py
 create mode 100644 backend/workers/mce-worker/tests/test_mce_wrapper_threads.py
 create mode 100644 docs/assets/tutorials/semantic-analysis/.gitkeep
 create mode 100644 docs/assets/tutorials/semantic-analysis/neo4j-groups.png
 create mode 100644 docs/assets/tutorials/semantic-analysis/neo4j-judge-reasoning.png
 create mode 100644 docs/assets/tutorials/semantic-analysis/trip-planner-timeline.png
 create mode 100644 docs/assets/tutorials/semantic-analysis/ui-group-details.png
 create mode 100644 docs/assets/tutorials/semantic-analysis/ui-outlier-sessions.png
 create mode 100644 docs/assets/tutorials/semantic-analysis/ui-semantic-groups.png
 create mode 100644 docs/tutorials/semantic-analysis.md
 create mode 100644 examples/trip-planner/README.md
 create mode 100644 examples/trip-planner/data/network.json
 create mode 100644 examples/trip-planner/load_traces.py
 create mode 100644 examples/trip-planner/pyproject.toml
 create mode 100644 examples/trip-planner/questions.py
 create mode 100644 examples/trip-planner/record_traces.py
 create mode 100644 examples/trip-planner/traces/trip_planner_sessions.jsonl.gz
 create mode 100644 examples/trip-planner/trip_planner.py
 create mode 100644 examples/trip-planner/uv.lock

diff --git a/README.md b/README.md
index 002f7f5..d72e402 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,7 @@ OTLP_HTTP_ENDPOINT=http://localhost:4318 uv run main.py 100
 
 The session shows up a few seconds after the example exits. The [first-trace tutorial](docs/tutorials/first-trace.md) walks through each step with screenshots, and explains how to query the knowledge graph.
 
-**Tear down** with `docker compose down` (add `-v` to also delete the data). To run SCOPE on Kubernetes instead, use the [Helm chart](deploy/k8s/charts/scope/README.md). To trace your own application, see [instrument your app](docs/tutorials/instrument-your-app.md).
+**Tear down** with `docker compose down` (add `-v` to also delete the data). To run SCOPE on Kubernetes instead, use the [Helm chart](deploy/k8s/charts/scope/README.md). To trace your own application, see [instrument your app](docs/tutorials/instrument-your-app.md); to see how SCOPE evaluates sessions against their peers, follow the [semantic analysis tutorial](docs/tutorials/semantic-analysis.md).
 
 ## How it works
 
@@ -56,7 +56,7 @@ The documentation site is built from [`docs/`](docs/index.md) with `mkdocs.yml`
 - **Ingestion**: [the telemetry path](docs/ingestion/index.md), [normalization](docs/ingestion/normalization/index.md) and [the ontology](https://ioc-project.github.io/scope/ontologies.html).
 - **Analysis**: [the analysis engines](docs/analysis/index.md), [pipelines](docs/analysis/pipelines.md), [workers](docs/analysis/workers/index.md) and [components](docs/analysis/components/index.md).
 - **Platform**: [the platform](docs/platform/index.md), [API](docs/platform/api/index.md) and [UI](docs/platform/ui/index.md).
-- **Tutorials**: [your first trace](docs/tutorials/first-trace.md), [instrument your app](docs/tutorials/instrument-your-app.md) and [deploy SCOPE](docs/tutorials/deploy-scope.md).
+- **Tutorials**: [your first trace](docs/tutorials/first-trace.md), [semantic analysis](docs/tutorials/semantic-analysis.md), [instrument your app](docs/tutorials/instrument-your-app.md) and [deploy SCOPE](docs/tutorials/deploy-scope.md).
 - Preview locally: `pip install -r docs/requirements.txt && mkdocs serve`.
 
 The ontology reference documentation (Widoco and WebVOWL) is generated separately:
diff --git a/backend/CHANGELOG.md b/backend/CHANGELOG.md
index d560283..74e9a3b 100644
--- a/backend/CHANGELOG.md
+++ b/backend/CHANGELOG.md
@@ -9,6 +9,23 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- **The `mce` worker computed one session at a time.** `MCEWrapper.process_mce_session` was `async` but called the blocking
+  evaluation directly, which held the event loop: whatever `MAX_INFLIGHT_MESSAGES` said, sessions went through one after the
+  other, about 8 seconds each with the default metrics. It now runs the evaluation in a thread, and the engine that the sessions
+  share is built once. A burst of 33 sessions had its metrics in 68 seconds instead of 260.
+- **Workers were capped at 16 sessions at a time, below their `MAX_INFLIGHT_MESSAGES`.** The handlers hand their blocking work to
+  `asyncio.to_thread`, whose pool holds `min(32, cpus + 4)` threads (16 on a 12-CPU host), so a worker allowed 32, 64 or 128
+  messages in flight never ran more than 16. `BaseWorker` now sizes the pool to `max_inflight_messages`.
+- **`Connection pool is full, discarding connection: clickhouse` in the logs.** The ClickHouse client of the workers shared
+  clickhouse-connect's default pool of 8 HTTP connections, and the `norm` worker, which reads the spans of many sessions
+  at once, opened more and threw the extra ones away after each request. `ClickHouseConnector` now has its own pool,
+  of 32 connections by default (`CLICKHOUSE_POOL_SIZE` sets it).
+- **Neo4j writes no longer lose a session to a deadlock.** When several workers wrote the same shared nodes at once (a
+  burst of sessions writing the same agents, tools and model), Neo4j rolled one of the transactions back with a
+  transient error (`DeadlockDetected`), and the `norm` worker dropped the whole session. `Neo4JConnector` now tries a query
+  again, up to five times with a short random wait, when Neo4j reports a transient error. The same burst on a new
+  database could also fail on the index that two sessions create at the same time
+  (`EquivalentSchemaRuleAlreadyExists`): that error is now what it says, the index exists.
 - **Normalization: identical boundary States are one State.** When a call started with what the previous step
   produced (a tool called with its agent's input, a tool returning what its agent returns, a handoff that passes
   its output on unchanged), `norm` kept two State nodes with the same content and no edge between them, so the
diff --git a/backend/api/scope/client/dal.py b/backend/api/scope/client/dal.py
index 1643b68..7ce485c 100644
--- a/backend/api/scope/client/dal.py
+++ b/backend/api/scope/client/dal.py
@@ -527,6 +527,10 @@ def _neo4j_props(values: Dict[str, Any]) -> Dict[str, Any]:
     return props
 
 
+# Neo4j's answer when an index that is being created already exists.
+_INDEX_ALREADY_EXISTS = "Neo.ClientError.Schema.EquivalentSchemaRuleAlreadyExists"
+
+
 def ingest_normalized_kg(
     db: Connector,
     nodes: List[Dict[str, Any]],
@@ -587,9 +591,12 @@ def ingest_normalized_kg(
                     f"FOR (n:{node_type}) ON (n.id)"
                 )
             except Exception as exc:
-                raise DatabaseError(
-                    f"Failed to ensure index for '{node_type}': {exc}"
-                ) from exc
+                # Two sessions can create the same index at once on a new database: the
+                # loser is told that it already exists, which is what it wanted.
+                if getattr(exc, "code", "") != _INDEX_ALREADY_EXISTS:
+                    raise DatabaseError(
+                        f"Failed to ensure index for '{node_type}': {exc}"
+                    ) from exc
             _indexed_node_types.add(node_type)
 
         if node_type in _NAME_KEYED_NODE_TYPES:
diff --git a/backend/api/scope/connectors/clickhouse.py b/backend/api/scope/connectors/clickhouse.py
index f75cccf..378ca1b 100644
--- a/backend/api/scope/connectors/clickhouse.py
+++ b/backend/api/scope/connectors/clickhouse.py
@@ -11,6 +11,9 @@
 
 logger = logging.getLogger(__name__)
 
+# HTTP connections kept open to ClickHouse, for the requests that run at the same time.
+DEFAULT_POOL_SIZE = 32
+
 
 class ClickHouseConnector(Connector):
     """Manages a ClickHouse connection via clickhouse-connect."""
@@ -22,20 +25,27 @@ def __init__(
         username: str = "default",
         password: str = "",
         database: str = "default",
+        pool_size: int = DEFAULT_POOL_SIZE,
     ):
         self._host = host
         self._port = port
         self._username = username
         self._password = password
         self._database = database
+        self._pool_size = pool_size
         self._client: Any = None  # clickhouse_connect.Client
 
     # ── lifecycle ─────────────────────────────────────────────────────────
 
     def connect(self) -> None:
         import clickhouse_connect
+        from clickhouse_connect.driver import httputil
 
+        # The client shares one HTTP connection pool across all the threads that use it, and
+        # clickhouse-connect's default pool keeps 8 connections: a worker that reads spans for
+        # many sessions at once opens more, and the extra ones are discarded after each request.
         self._client = clickhouse_connect.get_client(
+            pool_mgr=httputil.get_pool_manager(maxsize=self._pool_size),
             host=self._host,
             port=self._port,
             username=self._username,
diff --git a/backend/api/scope/connectors/neo4j.py b/backend/api/scope/connectors/neo4j.py
index 6c09692..80065a1 100644
--- a/backend/api/scope/connectors/neo4j.py
+++ b/backend/api/scope/connectors/neo4j.py
@@ -5,18 +5,32 @@
 from __future__ import annotations
 
 import logging
+import random
 import re
+import time as _time
 from datetime import date, datetime, time, timedelta
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional, TypeVar
 from urllib.parse import urlparse
 
 import neo4j.time
 from neo4j import GraphDatabase
+from neo4j.exceptions import TransientError
 
 from scope.connectors.base import Connector
 
 logger = logging.getLogger(__name__)
 
+T = TypeVar("T")
+
+# Neo4j rolls a transaction back and says "retry me" (TransientError) when two writers
+# collide, typically a deadlock on the nodes they share. Several workers write the same
+# structural nodes at once, so a query is tried again a few times before it fails.
+TRANSIENT_ATTEMPTS = 5
+TRANSIENT_BACKOFF_SECONDS = 0.2
+# The first retries are routine under concurrency (two sessions collide about once each), so they are
+# logged at INFO; from this attempt on, something is wrong enough to be worth a WARNING.
+TRANSIENT_WARN_FROM_ATTEMPT = 3
+
 
 SEMANTICGROUP_NODE_CHILDREN_NODES = "childrenNodes"
 SEMANTICGROUP_NODE_GROUP_NAME = "groupName"
@@ -137,6 +151,36 @@ def is_connected(self) -> bool:
 
     # ── query execution ───────────────────────────────────────────────────
 
+    @staticmethod
+    def _retry_transient(run: Callable[[], T]) -> T:
+        """Call ``run``, again after a short random wait when Neo4j reports a transient error.
+
+        A transient error means the transaction was rolled back, so running it again is safe.
+        """
+        for attempt in range(1, TRANSIENT_ATTEMPTS + 1):
+            try:
+                return run()
+            except TransientError as exc:
+                if attempt == TRANSIENT_ATTEMPTS:
+                    raise
+                delay = (
+                    TRANSIENT_BACKOFF_SECONDS
+                    * 2 ** (attempt - 1)
+                    * (0.5 + random.random())
+                )
+                logger.log(
+                    logging.WARNING
+                    if attempt >= TRANSIENT_WARN_FROM_ATTEMPT
+                    else logging.INFO,
+                    "Neo4j transient error (%s), trying again in %.2fs (attempt %d of %d)",
+                    getattr(exc, "code", "unknown"),
+                    delay,
+                    attempt,
+                    TRANSIENT_ATTEMPTS,
+                )
+                _time.sleep(delay)
+        raise AssertionError("unreachable")  # pragma: no cover
+
     def execute(self, query: str, params: Optional[Dict[str, Any]] = None) -> Any:
         self.ensure_connected()
         if logger.isEnabledFor(logging.DEBUG):
@@ -144,9 +188,13 @@ def execute(self, query: str, params: Optional[Dict[str, Any]] = None) -> Any:
                 "Executing query:\n***********************\n%s",
                 _render_query_with_params(query, params),
             )
-        with self._client.session(database=self._database) as session:
-            result = session.run(query, parameters=params or {})
-            return [_convert_neo4j_types(record.data()) for record in result]
+
+        def run() -> Any:
+            with self._client.session(database=self._database) as session:
+                result = session.run(query, parameters=params or {})
+                return [_convert_neo4j_types(record.data()) for record in result]
+
+        return self._retry_transient(run)
 
     def execute_command(
         self, command: str, params: Optional[Dict[str, Any]] = None
@@ -158,8 +206,12 @@ def execute_command(
                 "Executing command:\n***********************\n%s",
                 _render_query_with_params(command, params),
             )
-        with self._client.session(database=self._database) as session:
-            session.run(command, parameters=params or {}).consume()
+
+        def run() -> None:
+            with self._client.session(database=self._database) as session:
+                session.run(command, parameters=params or {}).consume()
+
+        self._retry_transient(run)
 
     @property
     def driver(self):
diff --git a/backend/api/scope/dependencies.py b/backend/api/scope/dependencies.py
index af097e8..221035c 100644
--- a/backend/api/scope/dependencies.py
+++ b/backend/api/scope/dependencies.py
@@ -20,7 +20,10 @@
     redis_lib = None
 
 from scope.connectors.base import Connector
-from scope.connectors.clickhouse import ClickHouseConnector
+from scope.connectors.clickhouse import (
+    DEFAULT_POOL_SIZE as DEFAULT_CLICKHOUSE_POOL_SIZE,
+    ClickHouseConnector,
+)
 from scope.connectors.neo4j import Neo4JConnector
 from scope.connectors.sqlalchemy import SQLAlchemyConnector
 from scope.core.config import settings
@@ -73,6 +76,9 @@ def _get_db_singleton() -> ClickHouseConnector:
             username=(os.getenv("CLICKHOUSE_USERNAME") or settings.CLICKHOUSE_USERNAME),
             password=os.getenv("CLICKHOUSE_PASSWORD", settings.CLICKHOUSE_PASSWORD),
             database=os.getenv("CLICKHOUSE_DATABASE", settings.CLICKHOUSE_DATABASE),
+            pool_size=int(
+                os.getenv("CLICKHOUSE_POOL_SIZE", str(DEFAULT_CLICKHOUSE_POOL_SIZE))
+            ),
         )
         _db_connector.connect()
         logger.info("ClickHouse singleton connector initialised")
diff --git a/backend/api/tests/test_clickhouse_pool.py b/backend/api/tests/test_clickhouse_pool.py
new file mode 100644
index 0000000..eb710a8
--- /dev/null
+++ b/backend/api/tests/test_clickhouse_pool.py
@@ -0,0 +1,117 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""The ClickHouse client keeps enough connections for the threads of a busy worker."""
+
+from __future__ import annotations
+
+import logging
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+
+import clickhouse_connect
+import pytest
+from clickhouse_connect.driver import httputil
+
+from scope import dependencies
+from scope.connectors.clickhouse import DEFAULT_POOL_SIZE, ClickHouseConnector
+
+
+@pytest.fixture
+def client_kwargs(monkeypatch):
+    seen: dict = {}
+
+    def fake_get_client(**kwargs):
+        seen.update(kwargs)
+        return object()
+
+    monkeypatch.setattr(clickhouse_connect, "get_client", fake_get_client)
+    return seen
+
+
+def _pool_size(kwargs: dict) -> int:
+    return kwargs["pool_mgr"].connection_pool_kw["maxsize"]
+
+
+def test_the_connector_gets_a_pool_of_the_size_it_is_given(client_kwargs) -> None:
+    ClickHouseConnector(pool_size=12).connect()
+
+    assert _pool_size(client_kwargs) == 12
+
+
+def test_the_default_pool_is_larger_than_the_one_of_the_client_library(
+    client_kwargs,
+) -> None:
+    ClickHouseConnector().connect()
+
+    assert (
+        _pool_size(client_kwargs)
+        == DEFAULT_POOL_SIZE
+        > httputil.default_pool_manager().connection_pool_kw["maxsize"]
+    )
+
+
+def test_the_pool_size_is_set_by_the_environment(monkeypatch) -> None:
+    built: dict = {}
+
+    class FakeConnector:
+        def __init__(self, **kwargs):
+            built.update(kwargs)
+
+        def connect(self):
+            pass
+
+    monkeypatch.setattr(dependencies, "ClickHouseConnector", FakeConnector)
+    monkeypatch.setattr(dependencies, "_db_connector", None)
+    monkeypatch.setenv("CLICKHOUSE_POOL_SIZE", "48")
+
+    dependencies._get_db_singleton()
+
+    assert built["pool_size"] == 48
+    monkeypatch.setattr(dependencies, "_db_connector", None)
+
+
+# ---------------------------------------------------------------- what the size changes
+
+
+class _Slow(BaseHTTPRequestHandler):
+    def do_GET(self) -> None:
+        time.sleep(0.3)  # long enough for all the requests to be in flight together
+        self.send_response(200)
+        self.send_header("content-length", "2")
+        self.end_headers()
+        self.wfile.write(b"ok")
+
+    def log_message(self, *args) -> None:
+        pass
+
+
+class _Server(ThreadingHTTPServer):
+    request_queue_size = 128  # the default backlog resets a burst of connections
+    daemon_threads = True
+
+
+def _discarded_connections(maxsize: int, requests: int, caplog) -> int:
+    server = _Server(("127.0.0.1", 0), _Slow)
+    threading.Thread(target=server.serve_forever, daemon=True).start()
+    manager = httputil.get_pool_manager(maxsize=maxsize)
+    url = f"http://127.0.0.1:{server.server_address[1]}/"
+    caplog.clear()
+    with caplog.at_level(logging.WARNING, logger="urllib3.connectionpool"):
+        with ThreadPoolExecutor(max_workers=requests) as pool:
+            list(
+                pool.map(lambda _: manager.request("GET", url).status, range(requests))
+            )
+    server.shutdown()
+    return sum(
+        "Connection pool is full" in record.getMessage() for record in caplog.records
+    )
+
+
+def test_more_requests_than_connections_discards_connections(caplog) -> None:
+    assert _discarded_connections(maxsize=4, requests=16, caplog=caplog) > 0
+
+
+def test_a_pool_as_large_as_the_requests_discards_none(caplog) -> None:
+    assert _discarded_connections(maxsize=16, requests=16, caplog=caplog) == 0
diff --git a/backend/api/tests/test_failure_reports.py b/backend/api/tests/test_failure_reports.py
index e172b08..9055416 100644
--- a/backend/api/tests/test_failure_reports.py
+++ b/backend/api/tests/test_failure_reports.py
@@ -53,7 +53,9 @@ def test_properties_are_the_ontology_model():
 
 
 def test_the_name_is_the_failure_name_or_the_code():
-    named = cg.failure_report_properties("s1", "CC-3", "low", "why", None, "Incomplete Synthesis")
+    named = cg.failure_report_properties(
+        "s1", "CC-3", "low", "why", None, "Incomplete Synthesis"
+    )
     unnamed = cg.failure_report_properties("s1", "CC-3", "low", "why")
 
     assert (named["name"], named["code"]) == ("Incomplete Synthesis", "CC-3")
diff --git a/backend/api/tests/test_neo4j_transient_errors.py b/backend/api/tests/test_neo4j_transient_errors.py
new file mode 100644
index 0000000..e5d8ee7
--- /dev/null
+++ b/backend/api/tests/test_neo4j_transient_errors.py
@@ -0,0 +1,192 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""Workers that write the same nodes at the same time must not lose data to a Neo4j deadlock."""
+
+from __future__ import annotations
+
+import logging
+
+import pytest
+from neo4j.exceptions import ClientError, Neo4jError, TransientError
+
+from scope.client import dal
+from scope.connectors import neo4j as connector_module
+from scope.connectors.neo4j import (
+    TRANSIENT_ATTEMPTS,
+    TRANSIENT_WARN_FROM_ATTEMPT,
+    Neo4JConnector,
+)
+from scope.core.exceptions import DatabaseError
+
+DEADLOCK = "Neo.TransientError.Transaction.DeadlockDetected"
+INDEX_EXISTS = "Neo.ClientError.Schema.EquivalentSchemaRuleAlreadyExists"
+
+
+def _error(code: str) -> Neo4jError:
+    """The exception that the driver raises for a server error code (its factory is named differently by version)."""
+    for name in ("hydrate", "_hydrate_neo4j"):
+        factory = getattr(Neo4jError, name, None)
+        if factory is not None:
+            return factory(code=code, message=f"{code} (test)")
+    raise RuntimeError("this neo4j driver has no way to build an error from a code")
+
+
+class FakeResult:
+    def __init__(self, rows):
+        self.rows = rows
+
+    def __iter__(self):
+        return iter(self.rows)
+
+    def consume(self):
+        return None
+
+
+class FakeRecord:
+    def __init__(self, data):
+        self._data = data
+
+    def data(self):
+        return self._data
+
+
+class FakeSession:
+    """Fails with ``errors`` (the first calls), then answers."""
+
+    def __init__(self, errors, rows):
+        self.errors, self.rows, self.calls = list(errors), rows, 0
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *exc):
+        return False
+
+    def run(self, query, parameters=None):
+        self.calls += 1
+        if self.errors:
+            raise self.errors.pop(0)
+        return FakeResult([FakeRecord(r) for r in self.rows])
+
+
+class FakeDriver:
+    def __init__(self, session):
+        self._session = session
+
+    def session(self, database=None):
+        return self._session
+
+
+@pytest.fixture
+def sleeps(monkeypatch):
+    waited: list[float] = []
+    monkeypatch.setattr(connector_module._time, "sleep", waited.append)
+    return waited
+
+
+def _connector(errors=(), rows=({"n": 1},)):
+    session = FakeSession(errors, list(rows))
+    connector = Neo4JConnector()
+    connector._client = FakeDriver(session)
+    connector.ensure_connected = lambda: None
+    return connector, session
+
+
+def test_a_deadlock_is_retried_until_the_write_goes_through(sleeps) -> None:
+    connector, session = _connector([_error(DEADLOCK), _error(DEADLOCK)])
+
+    connector.execute_command("MERGE (n:A {id: 1})")
+
+    assert session.calls == 3 and len(sleeps) == 2
+
+
+def test_a_read_is_retried_too(sleeps) -> None:
+    connector, session = _connector([_error(DEADLOCK)], rows=[{"n": 7}])
+
+    assert connector.execute("MATCH (n) RETURN n") == [{"n": 7}]
+    assert session.calls == 2
+
+
+def test_it_gives_up_after_the_last_attempt(sleeps) -> None:
+    connector, session = _connector([_error(DEADLOCK)] * (TRANSIENT_ATTEMPTS + 3))
+
+    with pytest.raises(TransientError):
+        connector.execute_command("MERGE (n:A {id: 1})")
+
+    assert session.calls == TRANSIENT_ATTEMPTS and len(sleeps) == TRANSIENT_ATTEMPTS - 1
+
+
+def test_the_wait_grows_with_each_attempt(sleeps) -> None:
+    connector, _ = _connector([_error(DEADLOCK)] * (TRANSIENT_ATTEMPTS - 1))
+
+    connector.execute_command("MERGE (n:A {id: 1})")
+
+    assert sleeps == sorted(sleeps) or sleeps[-1] > sleeps[0]
+    assert all(0 < wait < 10 for wait in sleeps)
+
+
+def test_the_first_retries_are_routine_and_the_later_ones_a_warning(
+    sleeps, caplog
+) -> None:
+    connector, _ = _connector([_error(DEADLOCK)] * (TRANSIENT_ATTEMPTS - 1))
+
+    with caplog.at_level(logging.INFO, logger="scope.connectors.neo4j"):
+        connector.execute_command("MERGE (n:A {id: 1})")
+
+    levels = [r.levelno for r in caplog.records if "transient error" in r.getMessage()]
+    assert levels == [
+        logging.WARNING if attempt >= TRANSIENT_WARN_FROM_ATTEMPT else logging.INFO
+        for attempt in range(1, TRANSIENT_ATTEMPTS)
+    ]
+    assert logging.INFO in levels and logging.WARNING in levels
+
+
+def test_an_error_that_is_not_transient_is_not_retried(sleeps) -> None:
+    connector, session = _connector([_error("Neo.ClientError.Statement.SyntaxError")])
+
+    with pytest.raises(ClientError):
+        connector.execute_command("MERGE (")
+
+    assert session.calls == 1 and not sleeps
+
+
+# ---------------------------------------------------------------- the index created by two sessions at once
+
+
+class FakeDb:
+    def __init__(self, index_error=None):
+        self.index_error, self.commands = index_error, []
+
+    def execute_command(self, command, params=None):
+        self.commands.append(command)
+        if command.startswith("CREATE INDEX") and self.index_error:
+            raise self.index_error
+
+    def execute(self, query, params=None):
+        return []
+
+
+@pytest.fixture(autouse=True)
+def _forget_indexes():
+    dal._indexed_node_types.clear()
+    yield
+    dal._indexed_node_types.clear()
+
+
+KG = ([{"id": "n1", "node_type": "Session"}], [])
+
+
+def test_an_index_that_another_session_just_created_is_not_an_error() -> None:
+    db = FakeDb(index_error=_error(INDEX_EXISTS))
+
+    assert dal.ingest_normalized_kg(db, *KG) is True
+
+    assert any(c.startswith("MERGE") or "MERGE" in c for c in db.commands)
+    assert "Session" in dal._indexed_node_types
+
+
+def test_another_error_on_the_index_still_fails() -> None:
+    db = FakeDb(index_error=_error("Neo.ClientError.Security.Forbidden"))
+
+    with pytest.raises(DatabaseError, match="ensure index"):
+        dal.ingest_normalized_kg(db, *KG)
diff --git a/backend/api/tests/test_structural_topology.py b/backend/api/tests/test_structural_topology.py
index e2f61a9..c5fc54a 100644
--- a/backend/api/tests/test_structural_topology.py
+++ b/backend/api/tests/test_structural_topology.py
@@ -24,8 +24,16 @@ def execute(self, query, params=None):
 
 
 AGENTS = [
-    {"agentId": "currency_assistant", "agentName": "currency_assistant", "agentDescription": "Converts"},
-    {"agentId": "rate_provider", "agentName": "rate_provider", "agentDescription": None},
+    {
+        "agentId": "currency_assistant",
+        "agentName": "currency_assistant",
+        "agentDescription": "Converts",
+    },
+    {
+        "agentId": "rate_provider",
+        "agentName": "rate_provider",
+        "agentDescription": None,
+    },
 ]
 
 
@@ -50,16 +58,25 @@ def test_the_agents_of_the_application_become_the_nodes() -> None:
 
 def test_an_application_without_agents_has_no_topology() -> None:
     assert _get_application_structural_topology(FakeDb([]), application_id="x") == {}
-    assert _get_application_structural_topology(FakeDb([{"agentId": ""}]), application_id="x") == {}
+    assert (
+        _get_application_structural_topology(
+            FakeDb([{"agentId": ""}]), application_id="x"
+        )
+        == {}
+    )
 
 
 def test_a_database_error_is_reported() -> None:
     with pytest.raises(DatabaseError, match="currency-demo"):
-        _get_application_structural_topology(FakeDb(error=RuntimeError("down")), application_id="currency-demo")
+        _get_application_structural_topology(
+            FakeDb(error=RuntimeError("down")), application_id="currency-demo"
+        )
 
 
 def test_the_query_is_scoped_to_the_application_through_its_mas() -> None:
-    query, params = ui_queries.structural_agents_by_application_query(application_id="app-1")
+    query, params = ui_queries.structural_agents_by_application_query(
+        application_id="app-1"
+    )
 
     assert "MAS {id: $applicationId}" in query and "belongsToMAS" in query
     assert "Processing" not in query and params == {"applicationId": "app-1"}
@@ -85,7 +102,9 @@ def _no_cache(monkeypatch):
 
 def _topology(declared):
     ui, kg = FakeClient(declared), FakeClient(None)
-    result = endpoints.application_topology("app", ui_client=ui, kg_client=kg, redis_client=None)
+    result = endpoints.application_topology(
+        "app", ui_client=ui, kg_client=kg, redis_client=None
+    )
     return result, ui, kg
 
 
@@ -107,7 +126,9 @@ def test_without_a_declared_graph_the_structural_one_is_returned(declared) -> No
 # ---------------------------------------------------------------- live topology of an application
 
 
-def test_the_live_sessions_of_an_application_match_the_prefixed_event_session_ids() -> None:
+def test_the_live_sessions_of_an_application_match_the_prefixed_event_session_ids() -> (
+    None
+):
     """Event session ids are "<application>_<uuid>"; the traces' session_id has no prefix."""
     from scope.query_builders import live_topology as lt
 
diff --git a/backend/mce/mce-client/src/mce/client/worker.py b/backend/mce/mce-client/src/mce/client/worker.py
index 3c089ce..44f1cf6 100644
--- a/backend/mce/mce-client/src/mce/client/worker.py
+++ b/backend/mce/mce-client/src/mce/client/worker.py
@@ -51,6 +51,7 @@
 import copy
 import logging
 import os
+import threading
 from dataclasses import replace
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Literal
@@ -319,6 +320,8 @@ def __init__(
             kg_provider=kg_provider,
         )
         self._restricted_engine: MetricEngine | None = None  # Lazy
+        # Sessions may be processed in several threads: only one of them builds the engine.
+        self._engine_lock = threading.Lock()
         scope_summary = ", ".join(
             f"{k}={len(v)}" for k, v in self._cfg.scope_metrics.items()
         )
@@ -434,65 +437,68 @@ def _get_restricted_engine(self) -> "MetricEngine":
         A shallow copy of each metric (and its metadata) is made so the global
         discovery cache is never mutated.
         """
-        if self._restricted_engine is None:
-            from mce.engine.engine import MetricEngine
-            from mce.core.registry.discovery import (
-                discover_all_metrics,
-                ensure_target_types,
-            )
+        with self._engine_lock:
+            if self._restricted_engine is None:
+                self._restricted_engine = self._build_restricted_engine()
+        return self._restricted_engine
 
-            # Build metric_id → YAML-declared ontology URIs.
-            metric_yaml_uris: dict[str, set[str]] = {}
-            for scope, names in self._cfg.scope_metrics.items():
-                uri = _YAML_SCOPE_TO_ONTOLOGY_URI.get(scope)
-                if uri is None:
-                    logger.warning(
-                        "Unknown YAML scope key '%s' — no ontology URI mapping. "
-                        "Metrics listed here will use their native target_types.",
-                        scope,
-                    )
-                    continue
-                for name in names:
-                    metric_yaml_uris.setdefault(name, set()).add(uri)
+    def _build_restricted_engine(self) -> "MetricEngine":
+        """Build the engine of :meth:`_get_restricted_engine`."""
+        from mce.engine.engine import MetricEngine
+        from mce.core.registry.discovery import (
+            discover_all_metrics,
+            ensure_target_types,
+        )
 
-            engine = MetricEngine(
-                max_workers=self._cfg.max_workers,
-                execution_strategy=self._cfg.execution_strategy,
-            )
-            for metric in discover_all_metrics():
-                if metric.metric_id not in self._cfg.all_metric_ids:
-                    continue
-                scoped = copy.copy(metric)
-                scoped.metadata = copy.copy(metric.metadata)
-                setattr(scoped, "_mce_max_workers", self._cfg.max_workers)
-                ensure_target_types(metric)
-                yaml_uris = metric_yaml_uris.get(metric.metric_id)
-                if yaml_uris:
-                    scoped.metadata.target_types = yaml_uris
-                else:
-                    scoped.metadata.target_types = copy.copy(
-                        metric.metadata.target_types
-                    )
-                engine.register_metric(scoped)
-            kg_provider = self._client._get_kg_provider()
-            engine.set_data_provider(kg_provider)
+        # Build metric_id → YAML-declared ontology URIs.
+        metric_yaml_uris: dict[str, set[str]] = {}
+        for scope, names in self._cfg.scope_metrics.items():
+            uri = _YAML_SCOPE_TO_ONTOLOGY_URI.get(scope)
+            if uri is None:
+                logger.warning(
+                    "Unknown YAML scope key '%s' — no ontology URI mapping. "
+                    "Metrics listed here will use their native target_types.",
+                    scope,
+                )
+                continue
+            for name in names:
+                metric_yaml_uris.setdefault(name, set()).add(uri)
 
-            from mce.cli.helpers.compute_setup import setup_cache_manager
+        engine = MetricEngine(
+            max_workers=self._cfg.max_workers,
+            execution_strategy=self._cfg.execution_strategy,
+        )
+        for metric in discover_all_metrics():
+            if metric.metric_id not in self._cfg.all_metric_ids:
+                continue
+            scoped = copy.copy(metric)
+            scoped.metadata = copy.copy(metric.metadata)
+            setattr(scoped, "_mce_max_workers", self._cfg.max_workers)
+            ensure_target_types(metric)
+            yaml_uris = metric_yaml_uris.get(metric.metric_id)
+            if yaml_uris:
+                scoped.metadata.target_types = yaml_uris
+            else:
+                scoped.metadata.target_types = copy.copy(metric.metadata.target_types)
+            engine.register_metric(scoped)
+        kg_provider = self._client._get_kg_provider()
+        engine.set_data_provider(kg_provider)
 
-            cache_manager = setup_cache_manager(
-                kg_provider,
-                no_cache_read=not self._cfg.cache_read,
-                no_cache_write=not self._cfg.cache_write,
-            )
-            engine.set_cache_manager(cache_manager)
+        from mce.cli.helpers.compute_setup import setup_cache_manager
 
-            self._restricted_engine = engine
-            logger.debug(
-                "Restricted engine built with %d metrics: %s",
-                len(self._cfg.all_metric_ids),
-                sorted(self._cfg.all_metric_ids),
-            )
-        return self._restricted_engine
+        cache_manager = setup_cache_manager(
+            kg_provider,
+            no_cache_read=not self._cfg.cache_read,
+            no_cache_write=not self._cfg.cache_write,
+        )
+        engine.set_cache_manager(cache_manager)
+
+        logger.debug(
+            "Restricted engine built with %d metrics: %s",
+            len(self._cfg.all_metric_ids),
+            sorted(self._cfg.all_metric_ids),
+        )
+        return engine
 
     def _compute_session_results(self, session_id: str) -> tuple[list, list]:
         """Run metric computation and return (passed, failed) results.
diff --git a/backend/mce/tests/client/test_worker_concurrency.py b/backend/mce/tests/client/test_worker_concurrency.py
new file mode 100644
index 0000000..c6c25fb
--- /dev/null
+++ b/backend/mce/tests/client/test_worker_concurrency.py
@@ -0,0 +1,47 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""Several sessions can be computed at once: the engine they share is built only once."""
+
+from __future__ import annotations
+
+import threading
+import time
+
+from mce.client.worker import MCEWorkerService
+
+
+def _service() -> MCEWorkerService:
+    service = MCEWorkerService.__new__(
+        MCEWorkerService
+    )  # no config or knowledge graph needed
+    service._restricted_engine = None
+    service._engine_lock = threading.Lock()
+    return service
+
+
+def test_threads_asking_at_once_build_the_engine_once(monkeypatch) -> None:
+    service = _service()
+    built: list[object] = []
+
+    def build() -> object:
+        time.sleep(0.1)  # long enough for the other threads to arrive
+        engine = object()
+        built.append(engine)
+        return engine
+
+    monkeypatch.setattr(service, "_build_restricted_engine", build)
+    engines: list[object] = []
+    threads = [
+        threading.Thread(
+            target=lambda: engines.append(service._get_restricted_engine())
+        )
+        for _ in range(8)
+    ]
+
+    for thread in threads:
+        thread.start()
+    for thread in threads:
+        thread.join()
+
+    assert len(built) == 1
+    assert len({id(engine) for engine in engines}) == 1 and len(engines) == 8
diff --git a/backend/norm/src/norm/ioa_observe/trajectory.py b/backend/norm/src/norm/ioa_observe/trajectory.py
index d1451cc..e860a9a 100644
--- a/backend/norm/src/norm/ioa_observe/trajectory.py
+++ b/backend/norm/src/norm/ioa_observe/trajectory.py
@@ -116,7 +116,14 @@ def bridge_states_if_mismatched(
         return
     if from_state.content == to_state.content:
         if from_state_id != to_state_id:
-            kept, dropped = (from_state_id, to_state_id) if keep == "from" else (to_state_id, from_state_id)
+            kept, dropped = (
+                (from_state_id, to_state_id)
+                if keep == "from"
+                else (
+                    to_state_id,
+                    from_state_id,
+                )
+            )
             registry.merge_node_into(State, dropped, kept)
         return
 
diff --git a/backend/norm/tests/test_state_continuity.py b/backend/norm/tests/test_state_continuity.py
index 2f8d74e..641e2bc 100644
--- a/backend/norm/tests/test_state_continuity.py
+++ b/backend/norm/tests/test_state_continuity.py
@@ -26,13 +26,17 @@
 
 from norm import load_otel_export, normalize, verify_kg
 from norm.ioa_observe.registry import Registry
-from norm.ioa_observe.trajectory import add_state_pair_transition, bridge_states_if_mismatched
+from norm.ioa_observe.trajectory import (
+    add_state_pair_transition,
+    bridge_states_if_mismatched,
+)
 
 FIXTURE = Path(__file__).resolve().parent / "fixtures" / "currency_demo.otel_export.jsonl"
 
 
 def _flow(nodes, edges):
-    """Nodes (States and Transitions) reachable from the session's first State through inputTo/leadsTo."""
+    """Nodes (States and Transitions) reachable from the session's first State through
+    inputTo/leadsTo."""
     by_id = {n["id"]: n for n in nodes}
     forward = defaultdict(set)
     for e in edges:
@@ -69,7 +73,9 @@ def test_every_state_is_on_the_chain_from_the_session_input(currency_kg) -> None
     reachable, final = _flow(nodes, edges)
     states = {n["id"] for n in nodes if n["node_type"] == "State"}
 
-    assert states == {i for i in reachable if i in states}, "States are cut off from the session's chain"
+    assert states == {i for i in reachable if i in states}, (
+        "States are cut off from the session's chain"
+    )
     assert final in reachable
 
 
@@ -78,16 +84,30 @@ def test_a_tool_shares_its_boundary_states_with_its_agent(currency_kg) -> None:
     by_id = {n["id"]: n for n in nodes}
 
     def boundary(call_name_part: str, edge_type: str) -> str:
-        call = next(n["id"] for n in nodes if call_name_part in n.get("name", "") and n["node_type"].endswith("Call"))
-        return next(e["target_id"] for e in edges if e["edge_type"] == edge_type and e["source_id"] == call)
+        call = next(
+            n["id"]
+            for n in nodes
+            if call_name_part in n.get("name", "") and n["node_type"].endswith("Call")
+        )
+        return next(
+            e["target_id"] for e in edges if e["edge_type"] == edge_type and e["source_id"] == call
+        )
 
     # lookup_rate is called with what rate_provider received and returns what it returns
-    assert boundary("rate_provider call", "hasInitialState") == boundary("lookup_rate", "hasInitialState")
-    assert boundary("rate_provider call", "hasFinalState") == boundary("lookup_rate", "hasFinalState")
+    assert boundary("rate_provider call", "hasInitialState") == boundary(
+        "lookup_rate", "hasInitialState"
+    )
+    assert boundary("rate_provider call", "hasFinalState") == boundary(
+        "lookup_rate", "hasFinalState"
+    )
     # convert_currency receives what currency_assistant received, but returns a number that is
     # not the sentence the agent writes: still two States, bridged
-    assert boundary("currency_assistant call", "hasInitialState") == boundary("convert_currency", "hasInitialState")
-    assert boundary("currency_assistant call", "hasFinalState") != boundary("convert_currency", "hasFinalState")
+    assert boundary("currency_assistant call", "hasInitialState") == boundary(
+        "convert_currency", "hasInitialState"
+    )
+    assert boundary("currency_assistant call", "hasFinalState") != boundary(
+        "convert_currency", "hasFinalState"
+    )
     assert by_id[boundary("convert_currency", "hasFinalState")]["content"] == "85.9"
 
 
@@ -144,7 +164,11 @@ def test_identical_states_are_merged_into_the_one_the_flow_comes_from() -> None:
     assert registry.edge_target(hasInitialState, "b") == final_a
     assert registry.edge_target(hasFinalState, "a") == final_a
     # b's transition now takes the merged State as input, and a's leads to it
-    assert (inputTo, final_a, registry.edge_target(correspondsToTransition, "b")) in registry.edges
+    assert (
+        inputTo,
+        final_a,
+        registry.edge_target(correspondsToTransition, "b"),
+    ) in registry.edges
     assert any(t is leadsTo and tid == final_a for t, _s, tid in registry.edges)
     assert (hasState, "b", final_a) in registry.edges
     assert not registry.all_of(ProcessingCall)
diff --git a/backend/worker-base/src/worker_base/base_worker.py b/backend/worker-base/src/worker_base/base_worker.py
index 4658cb4..117bfb8 100644
--- a/backend/worker-base/src/worker_base/base_worker.py
+++ b/backend/worker-base/src/worker_base/base_worker.py
@@ -7,6 +7,7 @@
 import logging
 import os
 import uuid
+from concurrent.futures import ThreadPoolExecutor
 from typing import Any, Callable
 
 import aio_pika
@@ -246,9 +247,24 @@ def emit_streamed_output(message):
             "output_path": output_path,
         }
 
+    def _size_thread_pool(self) -> None:
+        """Let as many blocking calls run at once as there are messages in flight.
+
+        Handlers hand their blocking work to ``asyncio.to_thread``, which uses the event loop's default
+        pool: ``min(32, cpus + 4)`` threads, 16 on a 12-CPU host. A worker allowed 64 messages in flight
+        would still process 16 at a time. The pool is sized to ``max_inflight_messages`` instead.
+        """
+        asyncio.get_running_loop().set_default_executor(
+            ThreadPoolExecutor(
+                max_workers=self.max_inflight_messages,
+                thread_name_prefix=f"{self.name or 'worker'}-handler",
+            )
+        )
+
     async def run_worker(self):
         connection = None
         inflight_tasks: set[asyncio.Task] = set()
+        self._size_thread_pool()
         try:
             connection = await aio_pika.connect_robust(self.rabbitmq_url)
             self.channel = await connection.channel()
diff --git a/backend/worker-base/tests/test_thread_pool.py b/backend/worker-base/tests/test_thread_pool.py
new file mode 100644
index 0000000..0057126
--- /dev/null
+++ b/backend/worker-base/tests/test_thread_pool.py
@@ -0,0 +1,57 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""A worker runs as many blocking handlers at once as it is allowed messages in flight."""
+
+import asyncio
+import threading
+import time
+
+import pytest
+
+from worker_base.base_worker import BaseWorker
+
+
+async def _peak_concurrency(tasks: int) -> int:
+    """The most ``asyncio.to_thread`` calls that were running at the same time."""
+    running = peak = 0
+    lock = threading.Lock()
+
+    def blocking() -> None:
+        nonlocal running, peak
+        with lock:
+            running += 1
+            peak = max(peak, running)
+        time.sleep(0.15)
+        with lock:
+            running -= 1
+
+    await asyncio.gather(*(asyncio.to_thread(blocking) for _ in range(tasks)))
+    return peak
+
+
+@pytest.mark.asyncio
+async def test_the_default_pool_of_the_event_loop_is_smaller_than_the_inflight_limit():
+    """What the workers had: the pool is min(32, cpus + 4), whatever the limit."""
+    from concurrent.futures import ThreadPoolExecutor
+
+    asyncio.get_running_loop().set_default_executor(ThreadPoolExecutor(max_workers=4))
+
+    assert await _peak_concurrency(24) <= 4
+
+
+@pytest.mark.asyncio
+async def test_the_pool_follows_max_inflight_messages():
+    worker = BaseWorker(rabbitmq_url="amqp://localhost", input_queue="q", max_inflight_messages=24)
+
+    worker._size_thread_pool()
+
+    assert await _peak_concurrency(24) == 24
+
+
+@pytest.mark.asyncio
+async def test_one_message_in_flight_runs_one_handler():
+    worker = BaseWorker(rabbitmq_url="amqp://localhost", input_queue="q", max_inflight_messages=1)
+
+    worker._size_thread_pool()
+
+    assert await _peak_concurrency(5) == 1
diff --git a/backend/workers/cognitive-failure-worker/src/cognitive_failure_worker/trace_file.py b/backend/workers/cognitive-failure-worker/src/cognitive_failure_worker/trace_file.py
index 47318fd..77f7c40 100644
--- a/backend/workers/cognitive-failure-worker/src/cognitive_failure_worker/trace_file.py
+++ b/backend/workers/cognitive-failure-worker/src/cognitive_failure_worker/trace_file.py
@@ -168,7 +168,9 @@ def __len__(self) -> int:
     @staticmethod
     def _label(summary: Mapping[str, Any]) -> str:
         """How the agent should cite a step: a description, its span and its id."""
-        ids = f"span {summary['span_id']}, id {summary['call_id']}" if summary["span_id"] else f"id {summary['call_id']}"
+        ids = (
+            f"span {summary['span_id']}, id {summary['call_id']}" if summary["span_id"] else f"id {summary['call_id']}"
+        )
         label = f"{summary['description']} ({ids})"
         if summary["kind"] == "llm" and summary["name"] and summary["name"] != summary["agent_id"]:  # the model
             label += f" [{summary['name']}]"
diff --git a/backend/workers/cognitive-failure-worker/tests/test_trace_file.py b/backend/workers/cognitive-failure-worker/tests/test_trace_file.py
index 0f93bea..9cf84ce 100644
--- a/backend/workers/cognitive-failure-worker/tests/test_trace_file.py
+++ b/backend/workers/cognitive-failure-worker/tests/test_trace_file.py
@@ -147,7 +147,9 @@ def test_grep_finds_text_with_its_step_field_and_context():
     with TraceFile(LONG) as trace:
         text = trace.grep("needle", context=10)
 
-    assert text.startswith("the 2nd planner LLM call (span span-3, id llmcall:3) [gpt-4o] input @51:") and "NEEDLE" in text
+    assert (
+        text.startswith("the 2nd planner LLM call (span span-3, id llmcall:3) [gpt-4o] input @51:") and "NEEDLE" in text
+    )
 
 
 def test_grep_limits_its_matches_and_says_so():
diff --git a/backend/workers/mce-worker/src/mce_worker/wrapper/mce_wrapper.py b/backend/workers/mce-worker/src/mce_worker/wrapper/mce_wrapper.py
index 6beff88..c11dd3e 100644
--- a/backend/workers/mce-worker/src/mce_worker/wrapper/mce_wrapper.py
+++ b/backend/workers/mce-worker/src/mce_worker/wrapper/mce_wrapper.py
@@ -5,6 +5,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import logging
 from pathlib import Path
 from typing import Any
@@ -116,6 +117,11 @@ def process_session(self, session_id: str) -> list[dict[str, Any]]:
     async def process_mce_session(
         self, session_id: str, local_file: str | None = None
     ) -> dict[str, list[dict[str, Any]]]:
-        """Async API compatible with BaseWorker.handle_message flow."""
+        """Async API compatible with BaseWorker.handle_message flow.
+
+        The evaluation is blocking (it waits on the metrics and the judge model), so it runs in a
+        thread: called directly it would hold the event loop, and the worker would handle one
+        session at a time whatever ``MAX_INFLIGHT_MESSAGES`` says.
+        """
         del local_file
-        return {"session_metrics": self.process_session(session_id)}
+        return {"session_metrics": await asyncio.to_thread(self.process_session, session_id)}
diff --git a/backend/workers/mce-worker/tests/test_mce_wrapper_threads.py b/backend/workers/mce-worker/tests/test_mce_wrapper_threads.py
new file mode 100644
index 0000000..7207966
--- /dev/null
+++ b/backend/workers/mce-worker/tests/test_mce_wrapper_threads.py
@@ -0,0 +1,51 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""The metrics of several sessions are computed at the same time, not one after the other."""
+
+import asyncio
+import time
+
+import pytest
+
+from mce_worker.wrapper.mce_wrapper import MCEWrapper
+
+
+def _wrapper(duration: float) -> MCEWrapper:
+    wrapper = MCEWrapper.__new__(MCEWrapper)  # the service behind it is not needed
+
+    def slow_session(session_id: str) -> list:
+        time.sleep(duration)  # a blocking evaluation, as with the metrics and the judge model
+        return [{"session": session_id}]
+
+    wrapper.process_session = slow_session
+    return wrapper
+
+
+@pytest.mark.asyncio
+async def test_sessions_are_computed_concurrently():
+    wrapper = _wrapper(duration=0.3)
+
+    started = time.monotonic()
+    results = await asyncio.gather(*(wrapper.process_mce_session(f"s{i}") for i in range(6)))
+    elapsed = time.monotonic() - started
+
+    assert [r["session_metrics"][0]["session"] for r in results] == [f"s{i}" for i in range(6)]
+    assert elapsed < 0.3 * 6 / 2, f"took {elapsed:.2f}s: the sessions ran one after the other"
+
+
+@pytest.mark.asyncio
+async def test_the_event_loop_stays_free_during_an_evaluation():
+    wrapper = _wrapper(duration=0.3)
+    ticks = 0
+
+    async def ticker():
+        nonlocal ticks
+        while True:
+            await asyncio.sleep(0.02)
+            ticks += 1
+
+    task = asyncio.create_task(ticker())
+    await wrapper.process_mce_session("s1")
+    task.cancel()
+
+    assert ticks >= 5
diff --git a/docs/analysis/components/worker-base.md b/docs/analysis/components/worker-base.md
index 24a561a..822f86a 100644
--- a/docs/analysis/components/worker-base.md
+++ b/docs/analysis/components/worker-base.md
@@ -60,7 +60,9 @@ Key methods/attributes a subclass uses:
 - `async run()` — starts the long-running RabbitMQ consumer (`run_worker()`): declares
   `input_queue`, sets QoS/prefetch from `max_inflight_messages`, and processes messages
   concurrently up to that limit, sending `start`/`complete`/`error` events to `feedback_queue` (if
-  set) around each `handle_message()` call.
+  set) around each `handle_message()` call. The event loop's thread pool, which `asyncio.to_thread` uses for
+  the blocking work of a handler, is sized to `max_inflight_messages` too: by default it would hold only
+  `min(32, cpus + 4)` threads and cap the worker below its limit.
 - `async run_argo_message(raw_input, output_path)` — single-message (or batch-of-messages, if
   `raw_input` is a JSON array) mode with no RabbitMQ involved: validates the input against
   `input_message_class`, calls `handle_message()`, and writes the serialized output message(s) to
diff --git a/docs/assets/tutorials/semantic-analysis/.gitkeep b/docs/assets/tutorials/semantic-analysis/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/docs/assets/tutorials/semantic-analysis/neo4j-groups.png b/docs/assets/tutorials/semantic-analysis/neo4j-groups.png
new file mode 100644
index 0000000000000000000000000000000000000000..885d76a9310810df14dbfca7e15d4eb44edc4436
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diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 3cb20c0..36e80a3 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -31,6 +31,16 @@ application, and follows it to the knowledge graph in Neo4j and to the dashboard
 [Follow your first trace from `docker compose up` to the dashboard](first-trace.md){ .md-button .md-button--primary }
 </div>
 
+## Semantic analysis
+
+How to evaluate a multi-agent system when there is no ground truth. The walkthrough loads the sessions of a trip
+planner, lets SCOPE group the ones that ask for the same thing, and uses each group as the point of comparison for
+the reliability, the normal behaviour and the anomalies of its sessions.
+
+<div class="button-row" markdown>
+[Evaluate sessions against their peers](semantic-analysis.md){ .md-button .md-button--primary }
+</div>
+
 ## Walkthroughs
 
 | Walkthrough | Status |
@@ -38,6 +48,7 @@ application, and follows it to the knowledge graph in Neo4j and to the dashboard
 | [Instrument your app](instrument-your-app.md) | Available |
 | [Deploying SCOPE](deploy-scope.md) | Available |
 | [Your first trace](first-trace.md) | Available |
+| [Semantic analysis](semantic-analysis.md) | Available |
 | Exploring a session in the dashboard | In preparation |
 | Reading cognitive-failure findings and their provenance | In preparation |
 | Consuming insights through the API | In preparation |
diff --git a/docs/tutorials/semantic-analysis.md b/docs/tutorials/semantic-analysis.md
new file mode 100644
index 0000000..0013381
--- /dev/null
+++ b/docs/tutorials/semantic-analysis.md
@@ -0,0 +1,329 @@
+# SCOPE — Tutorial: Semantic analysis
+
+<p class="eyebrow">Evaluate a multi-agent system without a ground truth, by comparing each session with its peers</p>
+
+## The problem: judging natural language without an answer key
+
+A multi-agent system is observed through its traces, and a trace tells you what the system *did*: which agents ran, which
+tools they called, how long it took and what it cost. It does not tell you whether the answer was any good. The answer is
+natural language, and natural language cannot be scored by the metrics of traditional monitoring: there is no
+status code for "the itinerary was wrong", and an exact-match test fails on two correct answers worded differently.
+
+The usual answer is an **LLM as a judge**: another model reads the question and the answer and scores them (relevancy,
+completeness, task completion, groundedness...). It works, but it has two limits that matter in production:
+
+- **It is expensive.** Every judged metric is a model call, for every session.
+- **A score alone says little.** Is a task completion of 0.7 good? Judges are stochastic, and a system that is
+  itself stochastic produces a different answer every time. With no **ground truth**, no expected answer written
+  for each question, there is nothing to compare the score to. Nobody writes the expected answer of every question
+  that users will ask.
+
+## The idea: compare each session with its peers
+
+If you cannot compare a session with the right answer, compare it with the other sessions that asked for the same
+thing. Sessions that ask similar questions, or talk about the same topic, can be **expected to behave alike**: each one
+is a reference for the others. SCOPE builds that reference in three steps, each answering a question that a single
+trace cannot:
+
+| Step | The question it answers | How | Where you see it |
+|---|---|---|---|
+| **Semantic grouping** | Which sessions are expected to behave alike? | Each session is embedded from its input, and the sessions whose embeddings are close form a group | **Explain** → **Active work** |
+| **Reliability** (consistency) | How much do the sessions of a group differ from each other? | The variability within the group, on three layers: the **metrics** (their spread), the **text** of the answers (the dispersion of their embeddings) and the **execution graph** (the distance between graphs), with a confidence level | *Consistency across sessions* on each metric card |
+| **Normal behaviour** | What does a typical session of the group look like? | A central value and a range for each metric, a representative answer, a representative execution graph | *Group performance*, and the expected range of the chart |
+| **Anomaly detection** | Which sessions depart from it? | An isolation forest on each layer, within the group | The red points, and the *Outliers Sessions* table |
+
+None of them needs the right answer. A judge score is no longer a grade to read alone, it is a signal that is read
+against the group: a task completion of 0.5 is an anomaly in a group where the sessions score 0.95, and ordinary in one
+where they score 0.5. The same comparison tells you where to spend the expensive evaluation: on the sessions and the
+groups that stand out. The [analysis overview](../analysis/index.md) describes the three dimensions that this makes
+possible, quality, reliability and performance.
+
+This walkthrough shows each step on a small system, from the first trace to the outlier that the analysis points at.
+
+## The example: a trip planner and a trap
+
+The [trip planner](https://github.com/ioc-project/scope/tree/main/examples/trip-planner) is a LangGraph MAS with a moderator and
+three specialist agents that answer questions about three fictional cities. Its questions come in three sets, each
+asking for the same thing in close wording:
+
+| Set | Example | Specialist |
+|---|---|---|
+| Routes | *What is the fastest route from Celestia to Luminos, and how many hours does the trip take?* | `itinerary_agent` |
+| Activities | *What are the best attractions and things to do in Celestia for a day of sightseeing?* | `activities_agent` |
+| Fares | *What is the ticket price in dollars from Celestia to Verdantia, and are there discounts?* | `concierge_agent` |
+
+Each set has eleven questions. Ten are ordinary. The eleventh is a trap: it asks about **Atlantis**, a city the planner
+does not know. It reads like its ten peers, and the system runs through the same steps, but it cannot answer it. That is
+a failure that no error rate shows (nothing crashed, the answer is a polite refusal) and that only the content of the
+answer reveals. You should end up with three groups, and the Atlantis sessions should stand out in them.
+
+You need:
+
+- A running SCOPE stack, started as in [Your first trace](first-trace.md), with an LLM endpoint configured in `.env`.
+  The embeddings, the names of the groups and the judge metrics come from it, so this walkthrough does not work
+  without one.
+- Python 3 to load the sessions. [`uv`](https://docs.astral.sh/uv/) and a model for the application itself are
+  only needed for the first step: the sessions come as a recording.
+
+!!! note "Keeping the cost down"
+    Besides the judge metrics of the metrics computation engine (MCE), which the analysis needs, every session goes through two more workers that
+    call your model for each session: the stateful evaluation and the cognitive-failure detection. To skip them
+    for this walkthrough, stop them before loading the sessions:
+
+    ```bash
+    cd deploy/docker-compose
+    docker compose stop stateful-eval cognitive-failure cognition-protocols
+    ```
+
+## 1. Run one trace of the trip planner
+
+The trip planner uses an OpenAI-compatible model: give it the key, and the URL if you go through a gateway.
+
+```bash
+cd examples/trip-planner
+export OPENAI_API_KEY=...             # and OPENAI_BASE_URL for a gateway; MODEL sets the model (gpt-4o)
+export OTLP_HTTP_ENDPOINT=http://localhost:4318
+uv run trip_planner.py "What is the fastest route from Celestia to Luminos, and how many hours does the trip take?"
+```
+
+```text
+[1/1] What is the fastest route from Celestia to Luminos, and how many hours does the trip take?
+    The fastest route from Celestia to Luminos takes 5 hours. There are two options: a direct train, or a trip by way of Verdantia, and both take the same amount of time.
+```
+
+Your answer is worded differently, it comes from your model. The moderator received the question and routed it to the
+itinerary agent, which called its `find_routes` tool, and the moderator wrote the answer from the reply. Each of
+those steps is a span, and each question is a session. The application builds its graph in a function the SDK
+decorates, so SCOPE also knows the topology of the system: the moderator, the three specialists and their tools.
+
+Open the dashboard at <http://localhost:8080> and find the session under **Applications** → `trip-planner` →
+**Inspect**. Its timeline shows the moderator, the itinerary agent, its tool and the calls to the model.
+
+![The timeline of one trip planner session](../assets/tutorials/semantic-analysis/trip-planner-timeline.png)
+
+## 2. Load the full set of sessions
+
+Grouping needs at least **20 sessions** with an embedding, so one trace is not enough. You could run the application
+thirty-three times (`uv run trip_planner.py --questions all` does, one question after the other, with about eight
+calls to your model for each), but the repository also holds the recording of thirty-three sessions, the eleven
+questions of each set, that you can load without running the application or calling any model.
+
+From `examples/trip-planner`:
+
+```bash
+uv run load_traces.py
+```
+
+```text
+33/33 sessions sent
+Loaded 33 sessions into http://localhost:4318
+```
+
+The script sends the recorded spans to the collector as new sessions, with new trace and session ids and the current
+time, so SCOPE processes them as if the application had just run them. It takes a few seconds, and it reads
+`OTLP_HTTP_ENDPOINT`, or `--endpoint`.
+
+Each session now goes through the pipeline: `norm` writes it to the knowledge graph, `embedding` embeds the states of
+its trajectory, and `mce` computes its metrics, among them the ones judged by your model. Check that all thirty-four
+are there (the one you ran yourself and the thirty-three you loaded), by opening **Inspect**, or from the logs, which
+count every session the `norm` worker has processed so far:
+
+```bash
+cd deploy/docker-compose
+docker compose logs norm | grep -c "Completed processing"
+```
+
+Give the metrics a few more minutes: the judged ones are the slowest.
+
+## 3. Trigger the grouping
+
+The grouping is done by the `hierarchical-grouping` worker. It wakes up every 15 minutes
+(`PERIODIC_TRIGGER_INTERVAL_SECONDS` in `docker-compose.yml`), and only groups an application once it has 20 sessions
+with an embedding (`HIERARCHICAL_GROUPING_MIN_SAMPLES`). Rather than wait, send it the message that its timer would
+send, with the name of the application. Use the `RABBITMQ_USERNAME` and `RABBITMQ_PASSWORD` of your `.env`:
+
+```bash
+curl -u "<RABBITMQ_USERNAME>:<RABBITMQ_PASSWORD>" -H 'content-type: application/json' \
+  -X POST http://localhost:15672/api/exchanges/%2F/amq.default/publish \
+  -d '{"properties":{},"routing_key":"new_session_to_periodic_grouping","payload":"{\"application_id\":\"trip-planner\"}","payload_encoding":"string"}'
+```
+
+```text
+{"routed":true}
+```
+
+(You can also do it from the RabbitMQ management page at <http://localhost:15672>: open the queue
+`new_session_to_periodic_grouping` and publish that payload.)
+
+The worker clusters the sessions, writes the groups to the knowledge graph, asks the model for a name and a summary of
+each leaf group, and hands each group to the `analysis` worker, which runs the consistency, normal behaviour and
+anomaly analyses on it. That takes a few minutes:
+
+```bash
+cd deploy/docker-compose
+docker compose logs hierarchical-grouping | grep "semantic groups"
+```
+
+```text
+Updating 5 semantic groups in knowledge graph: 5 additions - 0 deletions - 0 updates
+```
+
+## 4. The groups: who is compared with whom
+
+Open the application and go to **Explain** → **Active work**. The table lists the semantic groups, with their number of
+sessions and their reliability, quality and performance scores.
+
+![The semantic groups of the trip planner](../assets/tutorials/semantic-analysis/ui-semantic-groups.png)
+
+There are three groups, one for each set of questions, with eleven or twelve sessions each (twelve for the routes,
+which also hold the session you ran yourself). The names and summaries are written by your model, so they differ from
+run to run, for example *Fastest routes and travel time*, *Ticket price and discounts query* and *Best day trip
+attractions*. A summary is the question that the sessions have in common, with the details blanked out: *What is the
+fastest route between locations, and how many hours does the trip take?*
+
+The Atlantis sessions are in these groups. They were grouped by their question, and the question of an Atlantis session
+is as close to its group as the others are. That is the point: from the question alone, nothing sets them apart.
+
+The groups form a hierarchy: the routes and the fares questions, which are both about trains, are closer to each other
+than to the activities questions, so they share a parent group. The three groups you see are its leaves.
+
+## 5. Reliability: how much do the sessions of a group differ?
+
+Click a group. Its page has one card for each metric (the selector at the top chooses them), and each card reads the
+metric against the group.
+
+![A semantic group: one card per metric, with the group's behaviour, its consistency and the sessions](../assets/tutorials/semantic-analysis/ui-group-details.png)
+
+On a card:
+
+- **Group performance vs application average**: the normal value of the metric in this group, next to its average over
+  all the groups. More on it below.
+- **Consistency across sessions**: how alike the sessions of the group are on this metric, from 0% (they all differ) to
+  100% (they are identical). It is the **reliability** of the group on that metric: the variability that remains
+  after the sessions that ask for the same thing have been put together. Next to it, a **confidence** level (High,
+  Medium or Low) says how much to trust the figure: it is computed from a sample of a few sessions, and the fewer
+  the sessions, the wider the margin.
+- The chart places every session of the group on the metric. The dashed lines delimit the expected range, and the
+  sessions that the anomaly detection flagged (see step 7) are red.
+
+Look across the cards and the metrics of a group, and across the groups. In the run of this tutorial, the metrics of
+the execution (the number of cycles, the tool and model error rates, the workflow efficiency) are 100% consistent in
+every group: the trip planner runs the same steps whatever the question. What varies is what the answer is worth.
+The judged metrics (relevancy, completeness, task completion) are between 55% and 85% consistent, and the group of
+activities is the least reliable of the three, because the same kind of question gets answers of very uneven quality.
+
+The same figure means different things in different groups, and that is the reason for comparing within a group. In
+the run, the normal task completion of the routes group is 0.88 and that of the activities group is 0.47. A session of
+the activities group that scores 0.5 is typical, and the same score in the routes group would be a failure. A judge
+score read in isolation could not tell them apart.
+
+Consistency is computed on three layers, and the metrics are only one of them:
+
+| Layer | What is compared | What the run shows |
+|---|---|---|
+| **Metrics** | The spread of each metric over the sessions of the group | The cards above |
+| **Text** | The dispersion of the embeddings of the final answers | Between 78% and 89%: the answers of a group are alike, with their own wording |
+| **Execution graph** | The distance between the graphs of agents and tools that the sessions followed | 100%: every session followed the same graph |
+
+The last two are in the knowledge graph and in the API. In the Neo4j browser, one `ConsistencyReport` per group and layer
+(and per metric for the metrics):
+
+```cypher
+MATCH (c:ConsistencyReport)-[:ofSemanticGroup]->(g:SemanticGroup)
+WHERE g.groupName <> "" AND c.dataType IN ["text", "graph"]
+RETURN g.groupName AS grp, c.dataType AS layer, c.mean AS consistency, c.confidenceIndicator AS confidence
+ORDER BY grp, layer
+```
+
+## 6. Normal behaviour: what a typical session looks like
+
+The *group performance* value on each card, and the expected range of the chart, are the **normal behaviour** of the
+group: a central value and a range for each metric, taken from its sessions. It is also kept for the other layers, a
+representative answer and a representative execution graph, so that a group has a profile that you can show to a
+user, or compare a future session with. The normal behaviours are `NormalBehaviourReport` nodes, linked to their group:
+
+```cypher
+MATCH (n:NormalBehaviourReport)-[:ofSemanticGroup]->(g:SemanticGroup), (n)-[:aboutMetric]->(m:Metric)
+WHERE g.groupName <> "" AND m.name IN ["TaskCompletion", "AnswerRelevancy", "Duration"]
+RETURN g.groupName AS grp, m.name AS metric, n.centroid AS normal_value
+ORDER BY metric, grp
+```
+
+## 7. Anomalies: the sessions that depart from the group
+
+Anomalies are always **relative to a group**, never absolute: a session is an outlier because it differs from the sessions
+that did the same kind of work. On each layer, an isolation forest separates the typical sessions of the group from the
+ones that are easy to isolate. Scroll down the page of a group, to **Outliers Sessions**: it lists the sessions that the
+detector flagged on at least one metric, with their values.
+
+![The outlier sessions of a group, with the metrics on which they stand out](../assets/tutorials/semantic-analysis/ui-outlier-sessions.png)
+
+Read it as a list of candidates, not as a list of failures. The detection is statistical, on a group of about ten
+sessions, and the judged metrics are noisy: in the run of this tutorial, the detector also flagged ordinary sessions, on
+one metric or two, mostly in the group of activities, whose scores are the most uneven. The sessions to open first are
+the ones that stand out on several metrics at once, and these were the Atlantis sessions: in the routes group and in the
+fares group, the session about Atlantis is an outlier on task completion, response completeness and intent recognition
+at once. In the activities group, where even the ordinary sessions score unevenly, the Atlantis session is only
+flagged for its duration: that is what a low reliability does to the anomaly detection, and the figures of step 5
+warned of it.
+
+Open one (click it, then look at it in **Inspect**). The system did what it always does: the moderator, a specialist, the
+specialist's tool, the answer. Nothing failed. What is different is the answer, *"It seems that Atlantis is not a
+recognized city in the system..."*, and the judge saw it. Its scores, next to the ones of the peers of its group:
+
+| Metric | Atlantis session | Its group's other sessions (routes) |
+|---|---|---|
+| Answer relevancy | 0.00 | 0.89 on average |
+| Task completion | 0.00 | 0.96 on average |
+| Response completeness | 0.00 | 0.66 on average |
+
+Without the group, a task completion of 0.00 is a number. With it, it is the one session out of twelve that did not do
+what the other eleven did. The judge also writes down why, and you can read it in the knowledge graph:
+
+```cypher
+MATCH (r:MetricResult)-[:measures]->(s:Session)-[:hasInitialState]->(q:State), (r)-[:ofMetricType]->(d:Metric {name: "AnswerRelevancy"})
+WHERE q.content CONTAINS "Atlantis"
+RETURN q.content AS question, r.result AS score, r.reasoning AS why
+```
+
+![The judge's reasoning for the Atlantis sessions in the Neo4j browser](../assets/tutorials/semantic-analysis/neo4j-judge-reasoning.png)
+
+For the routes session it reads, for example, *"The score is 0.00 because the output fails to address the query about the fastest route from Celestia to Atlantis and the trip duration."* Yours will be worded differently.
+
+## 8. What to take from it
+
+- A trace is not enough to evaluate an agent, and a judge score is not enough to evaluate a trace. A **group of
+  comparable sessions** gives the score a meaning: how reliable the system is on this kind of question, what normal
+  looks like, and which sessions are not.
+- Everything here worked **without a ground truth**. No expected answer was written for any question.
+- The analysis tells you **where to look**. It does not say that the Atlantis sessions are failures (for a traveller who
+  mistyped a city, the refusal is the right answer); it says that they are the sessions that differ from their peers,
+  and you decide what that means.
+- The comparison makes the expensive evaluation **pay off**: the judge metrics are most useful where they are read
+  against a group, and the groups and sessions that stand out are where more evaluation, or a human, is worth the cost.
+
+## 9. Try it
+
+- Ask your own questions, of each kind, one at a time, then run the grouping again (step 3). A new route question
+  joins the routes group, and the analysis of the group is updated. A question about something else, such as the
+  weather in Luminos, is not put in any of the groups until enough similar sessions arrive.
+- Why the groups are what they are: two sessions are in the same leaf group when the embeddings of their inputs are
+  within a cosine distance of **0.15** (`HIERARCHICAL_GROUPING_MAX_DISTANCE`). The questions of a set are worded closely
+  on purpose, so they are near each other; looser paraphrases of the same request would spread over several small
+  groups. When the sessions change, the worker reuses the ids and names of the groups it already has instead of starting
+  over.
+
+## 10. Clean up
+
+```bash
+cd deploy/docker-compose
+docker compose start stateful-eval cognitive-failure cognition-protocols   # if you stopped them
+docker compose down -v                                                    # to delete the data as well
+```
+
+## Where to go next
+
+- [Your first trace](first-trace.md), if you have not followed it.
+- [Instrument your own application](instrument-your-app.md) and let SCOPE group and analyse its sessions.
+- [How the analysis works](../analysis/index.md), [the grouping](../analysis/workers/hierarchical-grouping-worker.md)
+  and [the analysis workers](../analysis/workers/analysis-worker.md).
diff --git a/examples/trip-planner/README.md b/examples/trip-planner/README.md
new file mode 100644
index 0000000..0aba818
--- /dev/null
+++ b/examples/trip-planner/README.md
@@ -0,0 +1,47 @@
+# Trip planner
+
+A small multi-agent system to try SCOPE with, built with [LangGraph](https://langchain-ai.github.io/langgraph/).
+A **moderator** reads a traveller's question about three fictional cities (Celestia, Verdantia and Luminos)
+and hands it to the specialist that can answer it, then writes the final answer from the reply:
+
+| Agent | Answers questions about | Tool |
+|---|---|---|
+| `itinerary_agent` | routes between cities and their travel times | `find_routes` |
+| `activities_agent` | what to see and do in a city | `read_city_guide` |
+| `concierge_agent` | train fares and discounts | `lookup_fares` |
+
+It is instrumented with the [AGNTCY Observe SDK](https://github.com/agntcy/observe): one `@graph` that builds
+the LangGraph graph (so SCOPE knows the topology of the system), one `@agent` per agent, one `@tool` per tool
+and one session per question. The calls to the model are traced by the SDK's LangChain instrumentation.
+
+## Run it
+
+It uses any OpenAI-compatible model.
+
+```bash
+cd examples/trip-planner
+export OPENAI_API_KEY=...                            # and OPENAI_BASE_URL for a gateway; MODEL sets the model (gpt-4o)
+export OTLP_HTTP_ENDPOINT=http://localhost:4318      # the OTLP/HTTP collector of your SCOPE deployment
+uv run trip_planner.py "What is the fastest route from Celestia to Luminos, and how many hours does the trip take?"
+```
+
+## Many sessions
+
+[`questions.py`](questions.py) has three sets of eleven questions: each set asks for the same thing in close
+wording, and the three sets ask for different things (a route, things to do, a ticket price). The last question
+of each set is a trap, about Atlantis, a city the planner does not know. They are what
+[the semantic analysis tutorial](../../docs/tutorials/semantic-analysis.md) groups and analyses.
+
+```bash
+uv run trip_planner.py --questions all               # one session per question, 33 in all
+```
+
+To skip the application and the model entirely, [`load_traces.py`](load_traces.py) replays 33 recorded
+sessions (`traces/trip_planner_sessions.jsonl.gz`) into the collector as new sessions. It needs nothing but
+Python:
+
+```bash
+python load_traces.py                                # --repeat 2 for 66 sessions
+```
+
+[`record_traces.py`](record_traces.py) records that dataset again, with your model.
diff --git a/examples/trip-planner/data/network.json b/examples/trip-planner/data/network.json
new file mode 100644
index 0000000..382a13e
--- /dev/null
+++ b/examples/trip-planner/data/network.json
@@ -0,0 +1,20 @@
+{
+  "cities": ["Celestia", "Verdantia", "Luminos"],
+  "routes": [
+    {"from": "Celestia", "to": "Verdantia", "hours": 3, "km": 245, "fare": 45},
+    {"from": "Verdantia", "to": "Luminos", "hours": 2, "km": 180, "fare": 35},
+    {"from": "Celestia", "to": "Luminos", "hours": 5, "km": 425, "fare": 75}
+  ],
+  "fare_rules": {
+    "currency": "USD",
+    "notes": "Standard class, one way. Fares are the same in both directions.",
+    "round_trip_discount_percent": 15,
+    "child_discount_percent": 50,
+    "senior_discount_percent": 30,
+    "premium_seating_upgrade": 15,
+    "night_train_sleeper_upgrade": 30,
+    "weekly_pass": 250,
+    "monthly_pass": 850,
+    "discounts_cannot_be_combined": true
+  }
+}
diff --git a/examples/trip-planner/load_traces.py b/examples/trip-planner/load_traces.py
new file mode 100644
index 0000000..e58d31c
--- /dev/null
+++ b/examples/trip-planner/load_traces.py
@@ -0,0 +1,110 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""Load recorded trip planner traces into SCOPE, without running the application.
+
+traces/trip_planner_sessions.jsonl.gz holds the spans of 33 sessions (eleven questions in each of three
+sets, see questions.py), recorded from real runs of trip_planner.py. This sends them to the
+OTLP/HTTP endpoint of a collector as new sessions: every replay gets fresh trace, span and session ids
+and current timestamps, so SCOPE processes them exactly as if the application had just run.
+Standard library only, no environment to set up:
+
+    python load_traces.py                  # the 33 sessions, once
+    python load_traces.py --repeat 2       # 66 sessions
+"""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+import os
+import re
+import secrets
+import time
+import urllib.request
+import uuid
+from pathlib import Path
+
+DATASET = Path(__file__).resolve().parent / "traces" / "trip_planner_sessions.jsonl.gz"
+
+
+def retarget(session: dict, now_ns: int) -> dict:
+    """The OTLP/JSON request of one recorded session, as a new session that ends at ``now_ns``.
+
+    Every trace, span and session id is replaced by a new one, wherever it appears: in the ids of the
+    spans, and also in the attributes where the SDK links an agent to the span or trace it runs in.
+    The traces of the session stay distinct, and every timestamp moves by the same shift.
+    """
+    spans = session["spans"]
+    shift = now_ns - max(int(s["endTimeUnixNano"]) for s in spans)
+
+    new_ids = {s["spanId"]: secrets.token_hex(8) for s in spans}
+    new_ids.update({t: secrets.token_hex(16) for t in {s["traceId"] for s in spans}})
+    prefix, _, _ = session["session_id"].rpartition("_")
+    new_ids[session["session_id"]] = (
+        f"{prefix}_{uuid.uuid4()}" if prefix else str(uuid.uuid4())
+    )
+
+    pattern = re.compile(
+        "|".join(re.escape(old) for old in sorted(new_ids, key=len, reverse=True))
+    )
+    spans = json.loads(
+        pattern.sub(lambda match: new_ids[match.group(0)], json.dumps(spans))
+    )
+
+    for span in spans:
+        span["startTimeUnixNano"] = str(int(span["startTimeUnixNano"]) + shift)
+        span["endTimeUnixNano"] = str(int(span["endTimeUnixNano"]) + shift)
+        for event in span.get("events", []):
+            event["timeUnixNano"] = str(int(event["timeUnixNano"]) + shift)
+    return {
+        "resourceSpans": [
+            {
+                "resource": session["resource"],
+                "scopeSpans": [
+                    {"scope": {"name": "trip-planner-replay"}, "spans": spans}
+                ],
+            }
+        ]
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument(
+        "--endpoint",
+        default=os.getenv("OTLP_HTTP_ENDPOINT", "http://localhost:4318"),
+        help="OTLP/HTTP endpoint of the collector",
+    )
+    parser.add_argument(
+        "--repeat", type=int, default=1, help="how many times to replay the dataset"
+    )
+    parser.add_argument(
+        "--pause",
+        type=float,
+        default=0,
+        help="seconds between two sessions",
+    )
+    args = parser.parse_args()
+
+    with gzip.open(DATASET, "rt", encoding="utf-8") as handle:
+        sessions = [json.loads(line) for line in handle]
+
+    total = len(sessions) * args.repeat
+    for number in range(1, total + 1):
+        request = retarget(sessions[(number - 1) % len(sessions)], time.time_ns())
+        http = urllib.request.Request(
+            args.endpoint.rstrip("/") + "/v1/traces",
+            data=json.dumps(request).encode(),
+            headers={"content-type": "application/json"},
+        )
+        urllib.request.urlopen(http, timeout=30).read()
+        print(f"\r{number}/{total} sessions sent", end="", flush=True)
+        time.sleep(args.pause)
+    print(f"\nLoaded {total} sessions into {args.endpoint}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/trip-planner/pyproject.toml b/examples/trip-planner/pyproject.toml
new file mode 100644
index 0000000..306427d
--- /dev/null
+++ b/examples/trip-planner/pyproject.toml
@@ -0,0 +1,11 @@
+[project]
+name = "trip-planner"
+version = "0.1.0"
+description = "A LangGraph moderator and three specialist agents answering trip questions, instrumented to be traced by SCOPE"
+requires-python = ">=3.11"
+dependencies = [
+    "ioa-observe-sdk==1.0.56",
+    "langchain>=1.0",
+    "langchain-openai>=0.3",
+    "langgraph>=0.6",
+]
diff --git a/examples/trip-planner/questions.py b/examples/trip-planner/questions.py
new file mode 100644
index 0000000..3ff646d
--- /dev/null
+++ b/examples/trip-planner/questions.py
@@ -0,0 +1,62 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""Questions for the trip planner, in three sets.
+
+The questions of a set ask for the same thing in slightly different words, so SCOPE's semantic grouping
+puts them in one group. The three sets ask for different things (a route and its travel time, what to
+do in a city, a ticket price), so they end up in three distinct groups. The wording is close inside a set
+on purpose: sessions are grouped by how near the embeddings of their questions are (a cosine distance of
+at most 0.15 by default), and looser paraphrases would spread over several groups.
+
+The last question of each set is a trap: it asks about Atlantis, a city the planner does not know. The
+question reads like its ten peers and is grouped with them, but the planner cannot answer it. That is the
+kind of session that the analysis of a group is meant to single out.
+"""
+
+QUESTIONS = {
+    # routes and travel times: handled by the itinerary agent
+    "itinerary": [
+        "What is the fastest route from Celestia to Luminos, and how many hours does the trip take?",
+        "What is the fastest route from Verdantia to Celestia, and how many hours does the trip take?",
+        "What is the fastest route from Luminos to Verdantia, and how many hours does the trip take?",
+        "Which route from Celestia to Verdantia has the shortest travel time in hours?",
+        "Which route from Luminos to Celestia has the shortest travel time in hours?",
+        "Which route from Verdantia to Luminos has the shortest travel time in hours?",
+        "How long is the journey from Celestia to Luminos, and which route is quickest?",
+        "How long is the journey from Verdantia to Celestia, and which route is quickest?",
+        "How long is the journey from Luminos to Verdantia, and which route is quickest?",
+        "Plan my route from Celestia to Verdantia and tell me the travel time in hours.",
+        # the trap
+        "What is the fastest route from Celestia to Atlantis, and how many hours does the trip take?",
+    ],
+    # what to see and do: handled by the activities agent
+    "activities": [
+        "What are the best attractions and things to do in Celestia for a day of sightseeing?",
+        "What are the best attractions and things to do in Luminos for a day of sightseeing?",
+        "What are the best attractions and things to do in Verdantia for a day of sightseeing?",
+        "Which attractions and activities should I do in Celestia during a day of sightseeing?",
+        "Which attractions and activities should I do in Luminos during a day of sightseeing?",
+        "Which attractions and activities should I do in Verdantia during a day of sightseeing?",
+        "What are the top sights to see and activities to do in Celestia on a day trip?",
+        "What are the top sights to see and activities to do in Luminos on a day trip?",
+        "What are the top sights to see and activities to do in Verdantia on a day trip?",
+        "Recommend the best attractions and things to do in Celestia for a day of sightseeing.",
+        # the trap
+        "What are the best attractions and things to do in Atlantis for a day of sightseeing?",
+    ],
+    # ticket prices: handled by the concierge agent
+    "fares": [
+        "How much does a ticket from Celestia to Luminos cost in dollars, and are there discounts?",
+        "How much does a ticket from Verdantia to Celestia cost in dollars, and are there discounts?",
+        "How much does a ticket from Luminos to Verdantia cost in dollars, and are there discounts?",
+        "What is the ticket price in dollars from Celestia to Verdantia, and are there discounts?",
+        "What is the ticket price in dollars from Luminos to Celestia, and are there discounts?",
+        "What is the ticket price in dollars from Verdantia to Luminos, and are there discounts?",
+        "What is the fare in dollars from Celestia to Luminos, one way or round trip, with discounts?",
+        "What is the fare in dollars from Verdantia to Celestia, one way or round trip, with discounts?",
+        "What is the fare in dollars from Luminos to Verdantia, one way or round trip, with discounts?",
+        "Tell me the ticket price in dollars from Celestia to Verdantia, with any discounts.",
+        # the trap
+        "What is the ticket price in dollars from Celestia to Atlantis, and are there discounts?",
+    ],
+}
diff --git a/examples/trip-planner/record_traces.py b/examples/trip-planner/record_traces.py
new file mode 100644
index 0000000..ea35738
--- /dev/null
+++ b/examples/trip-planner/record_traces.py
@@ -0,0 +1,83 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""Record the traces of the trip planner as the dataset that load_traces.py replays.
+
+It runs `trip_planner.py --questions all` against a local OTLP receiver, with the model you configure
+(OPENAI_API_KEY, OPENAI_BASE_URL, MODEL), and writes one line per session (its spans, as OTLP/JSON) to
+traces/trip_planner_sessions.jsonl.gz. The dataset in the repository was made with it; you only need it
+to record your own.
+
+    uv run record_traces.py
+"""
+
+from __future__ import annotations
+
+import gzip
+import json
+import os
+import subprocess
+import sys
+from collections import defaultdict
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from threading import Thread
+
+from google.protobuf.json_format import MessageToDict
+from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import ExportTraceServiceRequest
+
+OUT = Path(__file__).resolve().parent / "traces" / "trip_planner_sessions.jsonl.gz"
+requests: list[ExportTraceServiceRequest] = []
+
+
+class Receiver(BaseHTTPRequestHandler):
+    def do_POST(self) -> None:
+        body = self.rfile.read(int(self.headers.get("content-length", 0)))
+        if self.path.endswith("/v1/traces"):
+            if self.headers.get("content-encoding") == "gzip":
+                body = gzip.decompress(body)
+            requests.append(ExportTraceServiceRequest.FromString(body))
+        self.send_response(200)
+        self.send_header("content-type", "application/x-protobuf")
+        self.end_headers()
+
+    def log_message(self, *args) -> None:
+        pass
+
+
+def span_to_json(span) -> dict:
+    """OTLP/JSON of a span: MessageToDict encodes ids as base64, OTLP/JSON wants hex."""
+    data = MessageToDict(span)
+    data["traceId"], data["spanId"] = span.trace_id.hex(), span.span_id.hex()
+    data["parentSpanId"] = span.parent_span_id.hex()
+    for link, proto in zip(data.get("links", []), span.links):
+        link["traceId"], link["spanId"] = proto.trace_id.hex(), proto.span_id.hex()
+    return data
+
+
+def main() -> None:
+    server = ThreadingHTTPServer(("127.0.0.1", 0), Receiver)
+    Thread(target=server.serve_forever, daemon=True).start()
+    env = {**os.environ, "OTLP_HTTP_ENDPOINT": f"http://127.0.0.1:{server.server_address[1]}"}
+    subprocess.run([sys.executable, "trip_planner.py", "--questions", "all"], env=env, check=True)
+
+    sessions: dict[str, list[dict]] = defaultdict(list)
+    resource: dict = {}
+    for request in requests:
+        for resource_spans in request.resource_spans:
+            resource = MessageToDict(resource_spans.resource)
+            for scope_spans in resource_spans.scope_spans:
+                for span in scope_spans.spans:
+                    attributes = {kv.key: kv.value.string_value for kv in span.attributes}
+                    sessions[attributes["session.id"]].append(span_to_json(span))
+
+    OUT.parent.mkdir(exist_ok=True)
+    ordered = sorted(sessions.items(), key=lambda item: min(int(s["startTimeUnixNano"]) for s in item[1]))
+    with gzip.open(OUT, "wt", encoding="utf-8") as handle:
+        for session_id, spans in ordered:
+            spans.sort(key=lambda s: int(s["startTimeUnixNano"]))
+            handle.write(json.dumps({"session_id": session_id, "resource": resource, "spans": spans}) + "\n")
+    print(f"{len(ordered)} sessions, {sum(len(s) for _, s in ordered)} spans written to {OUT}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/trip-planner/traces/trip_planner_sessions.jsonl.gz b/examples/trip-planner/traces/trip_planner_sessions.jsonl.gz
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diff --git a/examples/trip-planner/trip_planner.py b/examples/trip-planner/trip_planner.py
new file mode 100644
index 0000000..c495dcf
--- /dev/null
+++ b/examples/trip-planner/trip_planner.py
@@ -0,0 +1,320 @@
+#  Copyright (c) 2026 Cisco Systems, Inc. and its affiliates
+#  SPDX-License-Identifier: Apache-2.0
+"""Trip planner: a LangGraph moderator and three specialist agents, traced with the AGNTCY Observe SDK.
+
+A traveller asks a question about three fictional cities (Celestia, Verdantia and Luminos). The moderator
+reads it and hands it to the specialist that can answer, and writes the final answer from the reply:
+
+    itinerary_agent    routes between the cities and their travel times      (find_routes)
+    activities_agent   what to see and do in each city                       (read_city_guide)
+    concierge_agent    train fares and discounts                             (lookup_fares)
+
+Each question is one SCOPE session. The model is any OpenAI-compatible one:
+
+    export OPENAI_API_KEY=...            # and OPENAI_BASE_URL for a gateway
+    uv run trip_planner.py "What is the fastest train route from Celestia to Luminos?"
+    uv run trip_planner.py --questions all     # one session per question of questions.py
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from pathlib import Path
+from typing import Literal, TypedDict
+
+from ioa_observe.sdk import Observe
+from ioa_observe.sdk.decorators import agent, graph, tool as observed_tool
+from ioa_observe.sdk.tracing import session_start
+from langchain.agents import create_agent
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, MessagesState, StateGraph
+from langgraph.types import Command
+
+APPLICATION_ID = "trip-planner"
+DATA = Path(__file__).resolve().parent / "data"
+NETWORK = json.loads((DATA / "network.json").read_text(encoding="utf-8"))
+CITIES = NETWORK["cities"]
+
+# ---------------------------------------------------------------------------------------------
+# Tools: what the specialists can look up
+# ---------------------------------------------------------------------------------------------
+
+
+class UnknownCity(ValueError):
+    pass
+
+
+def _city(name: str) -> str:
+    for city in CITIES:
+        if city.lower() == name.strip().lower():
+            return city
+    raise UnknownCity(f"Unknown city {name!r}. The cities are {', '.join(CITIES)}.")
+
+
+def _error(exc: Exception) -> str:
+    """What a tool answers when it cannot help: the model reads it and decides what to do."""
+    return json.dumps({"error": str(exc)})
+
+
+def _neighbours(city: str) -> list[tuple[str, int]]:
+    """The cities one train away from ``city``, with the travel time in hours."""
+    return [
+        (r["to"] if r["from"] == city else r["from"], r["hours"])
+        for r in NETWORK["routes"]
+        if city in (r["from"], r["to"])
+    ]
+
+
+@tool
+@observed_tool(
+    name="find_routes",
+    description="Lists the train routes between two cities with their travel time",
+    application_id=APPLICATION_ID,
+)
+def find_routes(origin: str, destination: str) -> str:
+    """Every train route from ``origin`` to ``destination`` with its travel time in hours, fastest first."""
+    try:
+        start, end = _city(origin), _city(destination)
+    except UnknownCity as exc:
+        return _error(exc)
+    routes: list[dict] = []
+
+    def walk(city: str, path: list[str], hours: int) -> None:
+        if city == end:
+            routes.append({"path": path, "hours": hours})
+            return
+        for neighbour, leg in _neighbours(city):
+            if neighbour not in path:
+                walk(neighbour, [*path, neighbour], hours + leg)
+
+    walk(start, [start], 0)
+    routes.sort(key=lambda r: (r["hours"], len(r["path"])))
+    return json.dumps({"origin": start, "destination": end, "routes": routes})
+
+
+@tool
+@observed_tool(
+    name="read_city_guide",
+    description="Returns the guide of a city: attractions and a suggested day",
+    application_id=APPLICATION_ID,
+)
+def read_city_guide(city: str) -> str:
+    """The guide of one city: its attractions with opening hours and prices, and a suggested day."""
+    try:
+        return (DATA / "guides" / f"{_city(city)}.txt").read_text(encoding="utf-8")
+    except UnknownCity as exc:
+        return _error(exc)
+
+
+@tool
+@observed_tool(
+    name="lookup_fares",
+    description="Returns the train fare between two cities and the discounts",
+    application_id=APPLICATION_ID,
+)
+def lookup_fares(origin: str, destination: str) -> str:
+    """The one-way and round-trip fare of the train between two cities, and the discounts and passes."""
+    try:
+        start, end = _city(origin), _city(destination)
+    except UnknownCity as exc:
+        return _error(exc)
+    for r in NETWORK["routes"]:
+        if {r["from"], r["to"]} == {start, end}:
+            rules = NETWORK["fare_rules"]
+            round_trip = round(
+                2 * r["fare"] * (1 - rules["round_trip_discount_percent"] / 100), 2
+            )
+            return json.dumps(
+                {
+                    "origin": start,
+                    "destination": end,
+                    "one_way": r["fare"],
+                    "round_trip": round_trip,
+                    "rules": rules,
+                }
+            )
+    return json.dumps({"error": f"No train runs directly between {start} and {end}."})
+
+
+# ---------------------------------------------------------------------------------------------
+# Agents
+# ---------------------------------------------------------------------------------------------
+
+llm = ChatOpenAI(
+    model=os.getenv("MODEL", "gpt-4o"),
+    temperature=0,
+    base_url=os.getenv("OPENAI_BASE_URL"),
+    api_key=os.getenv("OPENAI_API_KEY"),
+)
+
+SPECIALISTS = {
+    "itinerary_agent": (
+        [find_routes],
+        "Plans train routes between the cities and their travel times",
+        "You are an itinerary specialist. Use your tool to find the routes between two cities, and answer with "
+        "the fastest route first and its travel time in hours, then the alternatives.",
+    ),
+    "activities_agent": (
+        [read_city_guide],
+        "Recommends what to see and do in each city",
+        "You are a local guide. Use your tool to read the guide of the city, and recommend its attractions with "
+        "their opening hours and prices, in the order you would visit them.",
+    ),
+    "concierge_agent": (
+        [lookup_fares],
+        "Answers questions about train fares and discounts",
+        "You are a ticketing concierge. Use your tool to find the fare, and answer with the price in dollars, "
+        "then the discounts that apply to the traveller.",
+    ),
+}
+
+MODERATOR_PROMPT = (
+    "You are the moderator of a trip planning team. The specialists are:\n"
+    + "\n".join(
+        f"- {name}: {description}" for name, (_, description, _) in SPECIALISTS.items()
+    )
+    + "\n\nRead the conversation and decide which specialist should act next. Route to the one the question "
+    "needs, and only to the others if the question also needs them. Route to each specialist at most once. Once "
+    "a reply answers the question, do not route again: write the complete answer for the traveller in "
+    "`final_answer`, from the reply."
+)
+
+
+class Router(TypedDict):
+    """The moderator's decision."""
+
+    next: Literal["itinerary_agent", "activities_agent", "concierge_agent", "FINISH"]
+    final_answer: str
+    """The complete answer to the traveller. Only filled in with FINISH."""
+
+
+def _specialist_node(name: str):
+    tools, description, prompt = SPECIALISTS[name]
+    executor = create_agent(llm, tools=tools, system_prompt=prompt)
+
+    @agent(name=name, description=description, application_id=APPLICATION_ID)
+    def run(state: MessagesState) -> dict:
+        reply = executor.invoke(state)["messages"][-1].content
+        return {"role": "user", "content": reply, "name": name}
+
+    def node(state: MessagesState) -> Command[Literal["moderator"]]:
+        return Command(update={"messages": [run(state)]}, goto="moderator")
+
+    return node
+
+
+@agent(
+    name="moderator",
+    description="Routes the question to a specialist and writes the final answer",
+    application_id=APPLICATION_ID,
+)
+def decide_next(state: MessagesState) -> Router:
+    messages = [{"role": "system", "content": MODERATOR_PROMPT}] + state["messages"]
+    return llm.with_structured_output(Router).invoke(messages)
+
+
+def moderator_node(
+    state: MessagesState,
+) -> Command[
+    Literal["itinerary_agent", "activities_agent", "concierge_agent", "__end__"]
+]:
+    decision = decide_next(state)
+    answered = {getattr(message, "name", None) for message in state["messages"]}
+    if (
+        decision["next"] == "FINISH" or decision["next"] in answered
+    ):  # a specialist is asked once
+        content = decision.get("final_answer") or state["messages"][-1].content
+        return Command(
+            goto=END,
+            update={
+                "messages": [
+                    {"role": "assistant", "content": content, "name": "moderator"}
+                ]
+            },
+        )
+    return Command(goto=decision["next"])
+
+
+@graph(
+    name="trip_planner",
+    description="Answers questions about trips between Celestia, Verdantia and Luminos: routes, things to do and fares",
+    application_id=APPLICATION_ID,
+)
+def create_trip_planner():
+    """Build and compile the graph. Returned as is, so the SDK can record its topology."""
+    builder = StateGraph(MessagesState)
+    builder.add_edge(START, "moderator")
+    builder.add_node("moderator", moderator_node)
+    for name in SPECIALISTS:
+        builder.add_node(name, _specialist_node(name))
+    return builder.compile()
+
+
+def ask(question: str) -> str:
+    """One question, one SCOPE session."""
+    with session_start():
+        planner = create_trip_planner()
+        result = planner.invoke(
+            {"messages": [HumanMessage(content=question)]}, {"recursion_limit": 25}
+        )
+    return result["messages"][-1].content
+
+
+# ---------------------------------------------------------------------------------------------
+# Command line
+# ---------------------------------------------------------------------------------------------
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Ask the trip planner a question.",
+        epilog=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "question", nargs="?", help="the question (omit it with --questions)"
+    )
+    parser.add_argument(
+        "--questions",
+        choices=["all", "itinerary", "activities", "fares"],
+        help="ask the questions of questions.py: every set, or one",
+    )
+    parser.add_argument(
+        "--limit", type=int, default=0, help="ask at most this many of them"
+    )
+    args = parser.parse_args()
+
+    if args.questions:
+        from questions import QUESTIONS
+
+        sets = (
+            QUESTIONS
+            if args.questions == "all"
+            else {args.questions: QUESTIONS[args.questions]}
+        )
+        questions = [
+            q for group in zip(*sets.values()) for q in group
+        ]  # interleave the sets
+        questions = questions[: args.limit] if args.limit else questions
+    elif args.question:
+        questions = [args.question]
+    else:
+        parser.error("give a question, or --questions")
+    if not os.getenv("OPENAI_API_KEY"):
+        parser.exit(1, "Set OPENAI_API_KEY (and OPENAI_BASE_URL for a gateway).\n")
+
+    Observe.init(
+        APPLICATION_ID,
+        api_endpoint=os.getenv("OTLP_HTTP_ENDPOINT", "http://localhost:4318"),
+    )
+    for number, question in enumerate(questions, 1):
+        print(f"[{number}/{len(questions)}] {question}")
+        print(f"    {ask(question)}\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/trip-planner/uv.lock b/examples/trip-planner/uv.lock
new file mode 100644
index 0000000..9b39161
--- /dev/null
+++ b/examples/trip-planner/uv.lock
@@ -0,0 +1,4087 @@
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diff --git a/mkdocs.yml b/mkdocs.yml
index 3d53525..2b2fe9f 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -140,3 +140,4 @@ nav:
       - Instrument your app: tutorials/instrument-your-app.md
       - Deploy SCOPE: tutorials/deploy-scope.md
       - Your first trace: tutorials/first-trace.md
+      - Semantic analysis: tutorials/semantic-analysis.md