AutoGen

Targets the modern autogen-agentchat / autogen-core architecture (the actively maintained v0.4+ rewrite) - not the older pyautogen / ag2 fork. Install the integration extra:

pip install "agentx-python[autogen]"

`observer.run()`

Wraps an AssistantAgent or Team’s .run(task=...) call. Since AutoGen’s own API is async-native, observer.run() is async too - await it the same way you’d await agent.run(...).

from agentx import AgentX
from agentx.integrations.autogen import AgentXAutoGenObserver
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

agentx = AgentX.from_env()
observer = AgentXAutoGenObserver(
    tracer=agentx.tracer,
    name="support-agent",
    metadata={"env": "production"},
)

model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")
agent = AssistantAgent("assistant", model_client=model_client)

result = await observer.run(agent, task="How do I cancel my subscription?")
print(result.messages[-1].content)

agentx.tracer.flush(timeout=10)

Works the same way for a Team:

result = await observer.run(team, task="Research and summarize the cancellation policy.")

What gets traced

Each .run() call produces one trace.

Field	Source
`input`	`TaskResult.messages[0].content` - the task, normalized to a real message regardless of whether you passed a plain string or a message object
`output`	The last message’s `content`
`latencyMs`	Wall-clock time of the full `.run()` call
`performanceSummary`	One execution step per message with `models_usage` set (a real LLM call produced it), named by the speaking agent (`message.source`) so a multi-agent team reads as its agent-turn trajectory, plus one tool-call step per matched `ToolCallRequestEvent`/`ToolCallExecutionEvent` pair
`inputTokens` / `outputTokens`	Summed from each message’s `models_usage.prompt_tokens` / `.completion_tokens`
`error`	Exception message if `.run()` raises

Per-step timing is derived from each message’s real created_at timestamp, chained against the previous message’s timestamp. AutoGen’s message schema has no explicit start/end pair per LLM call, so this is real but approximate - the same caveat CrewAI’s per-task timing has.

observer.run() covers .run() only, not .run_stream() - a deliberate scope boundary, the same as this SDK’s OpenAI and Google Gen AI streaming coverage.

`AgentXAutoGenObserver` reference

AgentXAutoGenObserver(
    tracer: Tracer,
    name: str = "autogen-agent",
    metadata: dict | None = None,
    session_id: str | None = None,
)

Parameter	Description
`tracer`	`agentx.tracer` from your `AgentX` instance
`name`	Label shown in the AgentX UI
`metadata`	Static key-value metadata attached to every trace
`session_id`	Links traces from the same conversation thread

`observer.run()` parameters

observer.run(
    agent_or_team: AssistantAgent | Team,
    task: str | BaseChatMessage | Sequence[BaseChatMessage] | None = None,
    **kwargs,
) -> TaskResult

Any additional keyword arguments are passed straight through to agent_or_team.run(...).

Call tracer.flush() before your process exits in scripts or one-shot jobs. In long-running servers it is not required: traces drain automatically in the background.

Tracing

Framework Integrations

`observer.run()`

What gets traced

`AgentXAutoGenObserver` reference

`observer.run()` parameters

​observer.run()

​What gets traced

​AgentXAutoGenObserver reference

​observer.run() parameters

`observer.run()`

What gets traced

`AgentXAutoGenObserver` reference

`observer.run()` parameters