CrewAI

Auto-instrument CrewAI (crewai on PyPI) so every Crew.kickoff and Crew.kickoff_async inside an active AgentPing run emits one llm_call carrying the crew's aggregated token usage, request count and latency. Python only.

Install / enable

pip install "agentping-io[crewai]"

import agentping
from crewai import Agent, Crew, Task

agentping.init()
agentping.instrument_crewai()

crew = Crew(agents=[researcher, writer], tasks=[research, draft])

with agentping.run("weekly-brief", customer_id="acme-corp"):
    result = crew.kickoff(inputs={"topic": "Q3 churn"})

instrument_crewai() patches Crew.kickoff and Crew.kickoff_async at the class level. Idempotent and global; call it once at module load. Supported crewai versions: 1.x. Outside that range the patch is skipped with a single warning.

Events

CrewAI call AgentPing event What is recorded
Crew.kickoff, Crew.kickoff_async returning llm_call provider: "crewai", input and output tokens summed across every agent and task in the crew (from crew.usage_metrics), cached prompt tokens when reported, request_count (successful model requests), latency for the whole kickoff.
Crew.kickoff, Crew.kickoff_async raising llm_call with status: "error" provider: "crewai", the exception message and class, latency. The exception is re-raised.

This is deliberately coarse. CrewAI only reports usage at the crew level, and its agents can each use a different model, so the event carries no model and the provider is crewai. You get the crew's total tokens, how many requests it made and how long it took, which is enough to alert on runaway crews and to see cost per kickoff once you price it. kickoff_for_each is not patched directly; CrewAI runs it as one kickoff per input on a copy of the crew, so each input lands as its own llm_call.

Pricing

Because the event has no model, the shipped rate card does not price it. To see cost, add a rate card row for provider crewai with a blended per-million rate that matches the model your crew mostly uses, under Spend, Rate cards. Until then the kickoff shows in the unpriced-models list with its token counts. See Spend.

Naming

The run name is whatever you pass to agentping.run(...), typically the crew's job. Agent and task names inside the crew are not on the event; if you want them, emit your own step events from a task callback:

def on_task_done(output):
    r = agentping.active_run()
    if r:
        r.event("step", {"kind": "task", "task": output.description[:80], "agent": output.agent})

draft = Task(description="Write the brief", agent=writer, callback=on_task_done)

Source / notes

For per-agent and per-model detail, run OpenLLMetry (traceloop-sdk) or OpenInference's CrewAI instrumentation alongside and export to the OpenTelemetry endpoint; each model call then lands individually with its own model and tokens. Use one route or the other for spend so tokens are not counted twice.