agentmetrics-crewai 0.1.0

AgentMetrics observability integration for CrewAI agents

agentmetrics-crewai

AgentMetrics integration for CrewAI. Instantiate the listener once at startup and every crew.kickoff() reports back to your dashboard automatically showing latency, cost, token counts, tool calls, and errors, with zero changes to your crew code.

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Install

pip install agentmetrics-crewai

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Quickstart

from agentmetrics_crewai import AgentMetricsListener

# register once at startup, covers all crews in the process
AgentMetricsListener(
    agent_id="my-crew",
    base_url="http://localhost:8099",
)

# Run your crew as normal
result = MyCrew().kickoff()

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API

AgentMetricsListener(agent_id, base_url)

Parameter	Default	Description
agent_id	"crewai-agent"	Fallback label if the crew has no crew_name
base_url	"http://localhost:8099"	AgentMetrics server address

Instantiating the class auto-registers event handlers on the global crewai_event_bus with no further setup needed, and the listener handles concurrent kickoffs correctly via source_fingerprint tracking.

.flush(timeout=10.0)

Blocks until all in-flight HTTP requests complete. Call before process exit in scripts.

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What gets tracked

Each kickoff() call emits one event to /v1/events on completion or failure:

Field	Description
status	success or failed
duration_ms	Wall-clock kickoff duration
input_tokens / output_tokens	Aggregated across all LLM calls
cache_read_tokens / cache_write_tokens	Cache token counts
llm_calls	Number of LLM requests in the kickoff
tool_calls / tool_errors	Tool usage counts
tool_names	Set of tools invoked
model	Model name from the first LLM call
estimated_cost_usd	Computed from token counts and model pricing
error	First 500 chars of the error message on failure

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License

MIT