Observability
This lesson teaches how to make CrewAI multiagent applications transparent and debuggable using observability.
It covers CrewAI's built-in tracing (tracing=True on Crews and Flows), which sends execution data to the CrewAI AMP dashboard for inspecting agent decisions, task timelines, tool usage, LLM calls, costs, and errors .
Beyond built-in tracing, the lesson catalogs nine ecosystem observability tools (LangDB, OpenLIT, MLflow, Langfuse, Langtrace, Arize Phoenix, Portkey, Opik, and Weave) and provides a practical selection matrix to choose the right one based on project type—from solo MVPs (use AMP) to OTel-standardized orgs (use OpenLIT/Phoenix) to enterprise governance needs (use Portkey/LangDB) .
The full-stack integration pattern shows how to correlate a single run_id across FastAPI, SSE event streams, and trace backends so both developers and end-users get the visibility they need .
Watch the Video below