Monitoring for CrewAI Agents

Real-time agent performance, alerting, and SLA tracking in CrewAI agents — without rewriting your code, without adding a proxy to the request path.

The problem

CrewAI is great at what it does, but production-grade monitoring is bring-your-own. Most teams discover this when the first production incident hits, the first OpenAI bill arrives, or the first auditor asks for evidence.

Specifically:

Crew-level cost easily runs 5-10× single-agent cost — opaque without tooling
Role-based access control isn't enforced — any agent can call any tool
Long task chains can drift without continuous evaluation

How Prefactor solves it

Prefactor wraps your CrewAI Crew and adds monitoring as a runtime layer. Specifically:

alert on quality regression after a prompt change
alert when p95 latency exceeds SLA
monitor agent error rates per environment
track success rates per intent
dashboard for on-call view of agent health

Install and integrate

pip install prefactor-crewai

from crewai import Crew
from prefactor import Prefactor

pf = Prefactor(api_key="pf_live_...")
crew = pf.wrap(Crew(agents=[...], tasks=[...]), crew_id="research-crew")

Specific use cases

Alert on quality regression after a prompt change
Alert when p95 latency exceeds sla
Monitor agent error rates per environment
Track success rates per intent
Dashboard for on-call view of agent health

FAQ

Does this add latency? Per-span instrumentation adds ~2-5ms. Telemetry ships asynchronously. No synchronous network call in the agent's hot path unless you enable blocking policy enforcement.

Can I configure this per-agent? Yes. Monitoring settings are per-agent — different agents can have different policies, sampling rates, retention, etc.

Is this compatible with other observability tools? Yes. Prefactor exports OpenTelemetry; works alongside Datadog, Langfuse, LangSmith, and others.

Start free

[Get started free →] [Book a demo →]