CrewAI workflow telemetry

Test the integration

Send and verify events with CrewAI workflow telemetry

Use CrewAI workflow 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
Multi-agent workflow observability
2
Define the contract
run_id, crew_name, process_type, task_count, agent_count, and release
3
Log the final result
Wrap Crew.kickoff or the owning Flow method and emit one logical workflow outcome, rather than logging every internal message as a separate business event.
4
Check the stored event
Exercise a known fixture, then inspect agent_workflow_completed for one correctly typed terminal row.

Before you start

CrewAI and telemetry-sh initialized in a trusted Python runtime
Stable crew, process, task-category, and final-outcome names
A content policy covering task inputs, agent outputs, memory, knowledge, and tool payloads

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.

crewai-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.

crewai

CrewAI workflow telemetry event

python

from time import perf_counter
from uuid import uuid4

def run_research_crew(inputs):
    run_id = str(uuid4())
    started_at = perf_counter()
    status = "success"
    error_type = None

    try:
        return research_crew.kickoff(inputs=inputs)
    except Exception as error:
        status = "failed"
        error_type = classify_crew_error(error)
        raise
    finally:
        telemetry.log("agent_workflow_completed", {
            "run_id": run_id,
            "crew_name": "research_crew",
            "process_type": "sequential",
            "status": status,
            "error_type": error_type,
            "duration_ms": round((perf_counter() - started_at) * 1000),
            "release": APP_RELEASE,
        })

Event schema

run_id, crew_name, process_type, task_count, agent_count, and release

status, duration_ms, retry_count, handoff_count, and error_type

accepted, reviewer_outcome, estimated_cost_usd when approved, and environment

Check your setup

Checkpoint 1

Wrap Crew.kickoff or the owning Flow method and emit one logical workflow outcome, rather than logging every internal message as a separate business event.

Checkpoint 2

Use CrewAI's own tracing or callbacks when step-level diagnosis is required, and correlate with an approved run ID.

Checkpoint 3

Exercise partial task failure, delegation loops, human input, cancellation, and asynchronous kickoff behavior before defining alerts.

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.

crewai-verification

CrewAI workflow telemetry verification query

sql

SELECT *
FROM agent_workflow_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.

AI agent observability template Nested tool-call SQL Event sampling CrewAI crews CrewAI documentation

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 agent operations10 fields