Observability (OpenTelemetry tracing)¶

Note

Experimental: the spans and their attributes can change in a minor release of this provider. See Stable and experimental features.

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. 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 (AgentOperator, @task.agent / @task.llm and the SQL / branch / file-analysis / schema-compare operators), because they all build their agent through 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. The worker opens a task span before the operator runs, so the agent’s spans nest under it and share its trace_id. AgentOperator (and @task.agent) additionally stamps the task-instance identity on every GenAI span it emits: the five keys core tracing already puts on the task span (airflow.dag_id, airflow.task_id, airflow.dag_run.run_id, airflow.task_instance.try_number, airflow.task_instance.map_index) plus airflow.task_instance.id as the per-attempt run join key. So a span is filterable by dag, task, run, attempt, or map index directly, without walking up to the parent span (OpenTelemetry children inherit trace context, not attributes). 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.
Run join key. For an AgentOperator run, the task-instance id (unique per attempt, since Airflow regenerates it on each retry) is passed to pydantic-ai as the run’s run_id. It surfaces on the run’s GenAI spans as gen_ai.agent.call.id, and the operator also exposes it, alongside the run’s token usage, on XCom under the run_id and usage keys. usage is this attempt’s own usage – not the cross-attempt cumulative total described under usage_limits in Agents with tools: AgentOperator and @task.agent – and it is pushed on a failed attempt too, so a downstream all_done task or failure callback can read what the last attempt spent. With durable=True, steps an attempt replays from the cache are not part of it. XCom is cleared at the start of every attempt, so only the most recent attempt’s value survives, not each historical attempt’s. A downstream task can then reference the run (ti.xcom_pull(task_ids="my_agent", key="run_id")) and a trace backend can join a task’s output to its agent trace without parsing logs. With enable_hitl_review the run_id and usage reflect the initial model run, not the human-feedback regenerations. A run that resumes after a tool approval (see Approve an agent’s tool calls) continues as <task-instance id>-resumed, which is the run_id the operator pushes; usage covers both parts. Airflow 2 has no task-instance id, so there the key is <dag_id>/<run_id>/<task_id>/<map_index>/<try_number>, and spans carry the five identity keys without airflow.task_instance.id.
Scope. The run_id / usage XComs come only from AgentOperator and @task.agent, and so do the airflow.* identity attributes, apart from a Strands or ADK agent run inside agent_framework_tracing (see below). The other LLM operators still emit GenAI spans correlated to the task span by nesting, but without the identity attributes or the run join key.
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).
Cost is already on the span. pydantic-ai’s own instrumentation sets a best-effort operation.cost attribute on the model-call span whenever it can price the response – no provider configuration is needed for this.

Note

The agent-run span reports token usage under gen_ai.aggregated_usage.* while the per-model-call span keeps gen_ai.usage.*. This avoids double-counting in backends that sum a parent span and its children. Dashboards or alerts that read run-level token usage from gen_ai.usage.* should switch to gen_ai.aggregated_usage.*.

Enabling it¶

Enable core tracing and turn on the provider option:

[traces]
otel_on = True

[common.ai]
otel_export_enabled = True

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

# 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:

[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.

Agents built with other frameworks¶

Note

Experimental: agent_framework_tracing can change or be removed in a minor release of this provider. See Stable and experimental features.

Strands Agents and Google ADK emit OpenTelemetry spans of their own. Run the agent inside agent_framework_tracing() so those spans follow the same rules as AgentOperator’s:

from airflow.providers.common.ai.tools.strands import AirflowTools
from airflow.providers.common.ai.tools.tracing import agent_framework_tracing

with agent_framework_tracing():
    agent = Agent(model=model, plugins=[AirflowTools(warehouse)])
    answer = agent(question)

Inside the block the framework leaves prompts, completions and tool arguments and results out of its spans unless [common.ai] otel_export_enabled and capture_content are both on, using the switch each framework reads: the gen_ai_unredacted_attributes token of OTEL_SEMCONV_STABILITY_OPT_IN for Strands, and ADK_CAPTURE_MESSAGE_CONTENT_IN_SPANS and OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT for ADK. A value your deployment already set wins. Strands reads its switch once per process, when it creates its one tracer, so create the first Agent of the task inside the block, not at module level; ADK reads its switches when a TelemetryConfig is built, so build that inside the block too.

Every span started inside the block under the worker’s tracer provider carries the same airflow.* identity attributes as AgentOperator’s spans. When no tracer provider made a span for the task, the Dag run’s trace context is not made the parent of the framework’s spans: that context is marked as not sampled, and a parent-based sampler, the OpenTelemetry default, would drop every span a tracer provider the framework installs starts. When core tracing or auto-instrumentation made the task’s span, the Dag run’s sampling decision holds.

The identity attributes go on spans of the tracer provider that is installed when the block starts, so set up the framework’s own telemetry, such as StrandsTelemetry, before entering it. They follow the task through asyncio; ADK’s synchronous Runner.run runs the agent on a thread of its own that they do not reach, so use run_async.

Counting tool calls¶

Note

Experimental: the common_ai.tool_calls metric and its tags can change or be removed in a minor release of this provider. See Stable and experimental features.

Every call to one of this provider’s connection-backed toolsets increments the common_ai.tool_calls counter through Airflow’s metrics, whether the call comes from AgentOperator, a Pydantic AI agent you build yourself, or another framework through its adapter. It answers which toolsets are used and from where without reading task logs. The counter carries three tags:

Tag	Values
`toolset`	The toolset class, such as `SQLToolset` or `ObjectStorageToolset`.
`framework`	`pydantic_ai` for `AgentOperator` and your own Pydantic AI agents; `strands`, `adk` or `langchain` for a call through that framework’s adapter; `none` for a call to a toolset’s `airflow_tools()` made without an adapter.
`outcome`	`executed` when the call returned, `failed` when it raised (including a failure the model is asked to correct), and `replayed` when `AgentOperator(durable=True)` served the result from its cache on a retry. A call whose arguments fail validation never reaches the toolset and is not counted, and neither is a call that pauses the run until a person approves it.

Tags never include arguments, connection IDs, table names or paths. They reach backends that support them: OpenTelemetry metrics ([metrics] otel_on), or StatsD with [metrics] statsd_datadog_enabled or statsd_influxdb_enabled. The Agent Skills toolset, toolsets you write yourself and hand-built AirflowTool objects are not counted.

See Configuration Reference for the full list of options.