Pydantic AI agent telemetry

Test the integration

Send and verify events with Pydantic AI agent telemetry

Use Pydantic AI agent telemetry where your app knows the final result. Collect only the fields you need, then verify a test event before building charts.

1
Choose the outcome
Pydantic AI reliability
2
Define the contract
run_id, workflow, agent_name, provider, model_alias, and prompt_version
3
Log the final result
Wrap Agent.run to send one event when the run ends. Use Pydantic AI instrumentation for detailed model and tool traces.
4
Check the stored event
Exercise a known fixture, then inspect agent_run_completed for one correctly typed terminal row.

Before you start

pydantic-ai and telemetry-sh initialized in a trusted Python process
A stable workflow name and application-owned run identifier
An allowlist that excludes prompts, outputs, dependency objects, tool arguments, and model messages

Delivery setup

Install and initialize server-side

Initialize Telemetry for synchronous code or TelemetryAsync for asyncio code once per process with a server-side key. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.

pydanticai-install

pip installation

bash

python -m pip install telemetry-sh

1Create one reusable server-side client. Set its timeout and retry limit.
2Log an event when the operation succeeds, fails, retries, or times out.
3Send test events with known results and inspect the stored rows before enabling an alert.

Snippet

Start with one structured event

Add this shape where the workflow completes, fails, or retries. Then build the dashboard from real fields.

pydanticai

Pydantic AI agent telemetry event

python

from time import perf_counter

async def run_support_agent(prompt: str, run_id: str):
    started_at = perf_counter()
    status = "success"
    error_type = None

    try:
        result = await support_agent.run(prompt)
        return result.output
    except Exception as error:
        status = "failed"
        error_type = classify_agent_error(error)
        raise
    finally:
        await telemetry.log("agent_run_completed", {
            "run_id": run_id,
            "workflow": "support_resolution",
            "agent_name": "support_agent",
            "status": status,
            "error_type": error_type,
            "duration_ms": round((perf_counter() - started_at) * 1000),
            "release": APP_RELEASE,
        })

Event schema

run_id, workflow, agent_name, provider, model_alias, and prompt_version

status, duration_ms, retry_count, tool_call_count, and usage totals when approved

output_validated, accepted, human_handoff, error_type, release, and environment

Check your setup

Checkpoint 1

Wrap Agent.run to send one event when the run ends. Use Pydantic AI instrumentation for detailed model and tool traces.

Checkpoint 2

Treat output validation retries and provider retries as distinct categories when they lead to different engineering decisions.

Checkpoint 3

Do not serialize RunResult, messages, dependencies, tool arguments, or validated output into a Telemetry event.

Verification

Prove the event arrived

Run this after exercising known success and failure cases. Replace the fallback table name if your final event contract differs from the snippet.

pydanticai-verification

Pydantic AI agent telemetry verification query

sql

SELECT *
FROM agent_run_completed
ORDER BY timestamp_utc DESC
LIMIT 20;

Confirm one terminal row per logical outcome, with the expected status, identifiers, units, and UTC time.

Inspect the inferred schema and verify that retries do not change field types or generate a new logical event ID.

Search the stored fields for credentials, raw payloads, prompts, private content, and unbounded error messages.

Exercise a provider timeout, ingestion rejection, and process shutdown before treating the dashboard as complete.

Implementation references

Review the event contract, data-safety guidance, and upstream primary documentation before enabling a new production path.

Instrumentation testing in CI Sensitive-data controls Agent loop detection SQL Pydantic AI agents Pydantic AI testing

Where to log

Keep the outcome event small and recoverable

This pattern provides

Record the outcome as an event you can query with SQL.
Stable fields for dashboards, alerts, and cross-event correlation.
Test events for checking success, failure, retries, and timeouts.

This pattern does not provide

An OTLP exporter, automatic collection pipeline, or replacement for detailed traces and diagnostic logs.
Exactly-once delivery merely because the payload contains an event ID.
Permission to collect raw provider payloads, user content, credentials, or regulated data.

Delivery and idempotency Batching and shutdown Troubleshoot ingestion

Example event schemas

Check what each event records, when to send it, and which field types it needs. Review the example payload and privacy checklist before using it in production.

AI operations10 fields