Performance Monitoring & Optimization
This lesson teaches how to keep CrewAI multiagent workflows fast, predictable, and cost-efficient in production.
It covers the five core performance metrics to track—end-to-end latency, step latency, token usage, cost per run, and success rate—and shows how to surface them using CrewAI's built-in tracing (tracing=True).
You'll learn to reduce perceived latency with CrewAI streaming (real-time output chunks so users never stare at a blank screen), and apply execution controls like max_iter, max_execution_time, max_rpm, caching, and respect_context_window to prevent runaway runs and rate-limit crashes.
The lesson includes a repeatable optimization loop (baseline → identify bottlenecks via traces → apply one change → re-measure) and a full-stack FastAPI + React pattern that streams progress events and displays a live performance dashboard with time-to-first-token, wall time, token counts, and slowest task/tool widgets.
Watch the Video Below