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agentmetrics-anthropic

PyPI License: MIT

AgentMetrics integration for Claude Managed Agents (Python). Wrap your session event stream with one tracker and every session reports back to your dashboard when it terminates showing latency, cost, token counts with cache, tool calls, and errors.


Install

pip install agentmetrics-anthropic

Quickstart

import anthropic
from agentmetrics_anthropic import AgentMetricsSessionTracker

client  = anthropic.Anthropic()
tracker = AgentMetricsSessionTracker(
    agent_id="my-claude-agent",
    base_url="http://localhost:8099",
)

# Sync stream
with tracker.stream(client, session_id="sess_...") as stream:
    for event in stream:
        pass  # handle events as normal

tracker.flush()

Async

async with tracker.astream(client, session_id="sess_...") as stream:
    async for event in stream:
        pass

await tracker.flush()

API

AgentMetricsSessionTracker(agent_id, base_url)

Parameter Default Description
agent_id "anthropic-agent" Label shown in the dashboard
base_url "http://localhost:8099" AgentMetrics server address

.stream(client, session_id, **kwargs)

Returns a sync context manager. Yields the same events as client.beta.sessions.events.stream(). Emits a run summary on session.status_terminated.

.astream(client, session_id, **kwargs)

Async version of .stream().

.flush(timeout=10.0)

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


What gets tracked

Each session emits one event to /v1/events when it terminates:

Field Description
status success or failed
duration_ms Wall-clock session 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 session
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

License

MIT

Metadata

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