.. Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

.. http://www.apache.org/licenses/LICENSE-2.0

.. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Observability (OpenTelemetry tracing)

pydantic-ai ships native OpenTelemetry instrumentation that emits GenAI spans for each agent run, model call, and tool call, following the OpenTelemetry GenAI semantic conventions <https://opentelemetry.io/docs/specs/semconv/gen-ai/>__. When enabled, this provider turns that instrumentation on for every agent it builds and routes the spans through the OpenTelemetry exporter Airflow already uses, so they appear in whatever backend your deployment runs (Jaeger, Tempo, Grafana, Phoenix, Langfuse, an OTLP collector, ...), correlated to the task that produced them.

This covers all of the LLM operators (:class:~airflow.providers.common.ai.operators.agent.AgentOperator, @task.agent / @task.llm and the SQL / branch / file-analysis / schema-compare operators), because they all build their agent through :meth:~airflow.providers.common.ai.hooks.pydantic_ai.PydanticAIHook.create_agent.

How it works

No extra infrastructure. The provider does not configure an exporter or a TracerProvider of its own. It reuses the global provider that Airflow's core tracing installs, so the spans share the exporter and endpoint already configured under [traces] / the standard OTEL_EXPORTER_OTLP_* environment variables. If core tracing is not enabled in the worker process, no GenAI spans are emitted.
Correlation is automatic. The worker opens a task span before the operator runs, so the agent's spans nest under it and inherit the task's trace_id and airflow.* attributes (dag id, run id, task id, try number, map index). An automatic retry reuses the task instance's persisted trace context, so all attempts share one trace and appear as repeated task-run spans on it, distinguished by try number. Only a manual clear or rerun regenerates the context and starts a new trace.
Content is off by default. Only token counts, model id, latency, tool names, and finish reason are recorded. Prompt and completion text is never emitted unless you opt in (see below).

Enabling it

Enable core tracing and turn on the provider option:

.. code-block:: ini

[traces]
otel_on = True

[common.ai]
otel_export_enabled = True

Configure the exporter destination with the standard OpenTelemetry environment variables, for example:

.. code-block:: bash

# Core tracing defaults the exporter to OTLP/gRPC. For an OTLP/HTTP
# endpoint (port 4318, ``/v1/traces`` path) also select the HTTP exporter:
export OTEL_TRACES_EXPORTER="otlp_proto_http"
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT="http://otel-collector:4318/v1/traces"

Capturing prompt and completion content

By default the spans carry no message text. To also record model inputs and outputs (gen_ai.input.messages / gen_ai.output.messages), set:

.. code-block:: ini

[common.ai]
capture_content = True

.. warning::

With ``capture_content`` enabled, prompts, completions, and tool IO are
exported to your tracing backend **without redaction**. Airflow's secret masking
applies to logs and rendered template fields, not to OpenTelemetry span
attributes, so it does not scrub this content. Enable it only for debugging
in a trusted environment. It has no effect unless ``otel_export_enabled`` is
``True``.

See :doc:configurations-ref for the full list of options.