diff --git a/README.md b/README.md index 9ad5e50..d72e402 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* | | +| The knowledge graph, in the Neo4j browser (`neo4j` and your `NEO4J_PASSWORD`) | | -- 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); to see how SCOPE evaluates sessions against their peers, follow the [semantic analysis tutorial](docs/tutorials/semantic-analysis.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 @@ -43,13 +56,28 @@ 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: - **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). 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 "_"; 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/dashboard.png b/docs/assets/dashboard.png new file mode 100644 index 0000000..ace3fc8 Binary files /dev/null and b/docs/assets/dashboard.png differ 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 0000000..885d76a Binary files /dev/null and b/docs/assets/tutorials/semantic-analysis/neo4j-groups.png differ diff --git a/docs/assets/tutorials/semantic-analysis/neo4j-judge-reasoning.png b/docs/assets/tutorials/semantic-analysis/neo4j-judge-reasoning.png new file mode 100644 index 0000000..2f73db1 Binary files /dev/null and b/docs/assets/tutorials/semantic-analysis/neo4j-judge-reasoning.png differ diff --git a/docs/assets/tutorials/semantic-analysis/trip-planner-timeline.png b/docs/assets/tutorials/semantic-analysis/trip-planner-timeline.png new file mode 100644 index 0000000..ca7153f Binary files /dev/null and b/docs/assets/tutorials/semantic-analysis/trip-planner-timeline.png differ diff --git a/docs/assets/tutorials/semantic-analysis/ui-group-details.png b/docs/assets/tutorials/semantic-analysis/ui-group-details.png new file mode 100644 index 0000000..87ec10b Binary files /dev/null and b/docs/assets/tutorials/semantic-analysis/ui-group-details.png differ diff --git a/docs/assets/tutorials/semantic-analysis/ui-outlier-sessions.png b/docs/assets/tutorials/semantic-analysis/ui-outlier-sessions.png new file mode 100644 index 0000000..e1ba500 Binary files /dev/null and b/docs/assets/tutorials/semantic-analysis/ui-outlier-sessions.png differ diff --git a/docs/assets/tutorials/semantic-analysis/ui-semantic-groups.png b/docs/assets/tutorials/semantic-analysis/ui-semantic-groups.png new file mode 100644 index 0000000..f03b1d8 Binary files /dev/null and b/docs/assets/tutorials/semantic-analysis/ui-semantic-groups.png differ diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index dcd69a3..60e185d 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -39,6 +39,16 @@ 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 } +## 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. + +
+[Evaluate sessions against their peers](semantic-analysis.md){ .md-button .md-button--primary } +
+ ## Walkthroughs | Walkthrough | Status | @@ -47,6 +57,7 @@ application, and follows it to the knowledge graph in Neo4j and to the dashboard | [Instrument an OpenClaw-style MAS](instrument-openclaw.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 + +

Evaluate a multi-agent system without a ground truth, by comparing each session with its peers

+ +## 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 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 ":" -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 : 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 new file mode 100644 index 0000000..6b221b6 Binary files /dev/null and b/examples/trip-planner/traces/trip_planner_sessions.jsonl.gz differ 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", 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nav: - Instrument OpenClaw-style MAS: tutorials/instrument-openclaw.md - Deploy SCOPE: tutorials/deploy-scope.md - Your first trace: tutorials/first-trace.md + - Semantic analysis: tutorials/semantic-analysis.md