Guide

Monitor your AutoGen agents in production.

AutoGen agents converse and call the model many times to reach an answer. AgentPing instruments the underlying provider and wraps the conversation in a run, so one record carries the cost, status and output.

Track your first AI workflow Jump to setup

Two lines, then every run reports in.

Install the SDK and point it at your AutoGen agent. One record lands per run with the cost, status, latency and output behind it.

shellinstall
pip install agentping
pythonmonitor your agent
import agentping

agentping.init(api_key="apk_…", agent="autogen-team")
agentping.instrument_openai()  # or instrument_anthropic()

# Wrap the conversation so the model calls roll up into one run.
with agentping.run(agent="autogen-team") as run:
    result = await team.run(task="…")

AutoGen has no native hook, so you instrument the provider and wrap the conversation; the model calls inside roll up into the run. Full reference is in the docs.

What AgentPing tracks for AutoGen.

  • Cost per conversation, rolled up across every model call the agents make.
  • A missed-run alert when a scheduled job stops running.
  • Output quality on the final answer, scored against your rubric.

See the pillars behind these: Spend, Pulse and Verify.

Why instrument the provider instead of AutoGen directly?
AutoGen does not expose a callback hook the way LangChain or CrewAI do, so the reliable path is to instrument the model provider (OpenAI, Anthropic) and wrap the conversation in agentping.run(). Every model call inside is attributed to that run.
Does the run capture the whole conversation?
Yes. All the model calls made inside the agentping.run() block roll up into a single run with the combined cost.
Will it slow the conversation down?
No. Telemetry runs off the hot path with a hard timeout and a bounded queue.

Monitor your AutoGen agents in minutes.

Add the SDK, send one run, and watch cost, status and quality land in the dashboard.

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