> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fact0.io/llms.txt
> Use this file to discover all available pages before exploring further.

# AutoGen

> Trace multi-agent AutoGen conversations and audit tool calls.

# AutoGen

<Note>
  Native AutoGen helpers are on the roadmap. The pattern below works today with `pyautogen >= 0.2`.
</Note>

## Install

```bash theme={null}
pip install fact0-sdk pyautogen
```

## Pattern

One execution per chat, one span per agent turn.

```python theme={null}
import fact0
from autogen import ConversableAgent, GroupChat, GroupChatManager

client = fact0.Client(api_key="f0_live_...")

planner = ConversableAgent("planner", llm_config={"model": "gpt-4o"})
coder = ConversableAgent("coder",   llm_config={"model": "gpt-4o"})

group = GroupChat(agents=[planner, coder], messages=[], max_round=10)
manager = GroupChatManager(groupchat=group, llm_config={"model": "gpt-4o"})

with client.telemetry.execution(agent_id="autogen.refactor") as ex:
    def hook(recipient, messages, sender, config):
        with ex.span(f"{sender.name}->{recipient.name}", span_type="MODEL_INVOCATION") as span:
            span.complete(output={"last": messages[-1]["content"][:500]})
        return False, None  # continue normal flow

    for a in [planner, coder, manager]:
        a.register_reply([ConversableAgent, None], hook, position=0)

    planner.initiate_chat(manager, message="Refactor invoice service")
```

## Audit tool calls

```python theme={null}
def on_tool_call(name: str, args: dict):
    client.audit.log(
        actor={"id": "agent.coder", "type": "agent"},
        action=f"tool.{name}",
        resource={"id": args.get("target", "n/a"), "type": "code"},
        outcome="success",
        metadata=args,
    )
```

## Related

* [Concepts: executions](/concepts/executions)
* [LangGraph](/integrations/langgraph) for graph-shaped multi-agent flows