AutoGen (legacy API)

The original AutoGen API (autogen.ConversableAgent, initiate_chat, and related classes) is now maintained by AG2. Trace that API with the supported AG2Instrumentor, which captures chats, replies, and tool execution as OpenInference AGENT and TOOL spans in Arize AX. The deprecated openinference-instrumentation-autogen package is not supported.

This page is for applications using the legacy autogen API. Install AG2, which exposes that module, rather than the original AutoGen package. For Microsoft’s current autogen-agentchat package, use the separate AutoGen AgentChat tracing guide.

Prerequisites

Python 3.10+
An Arize AX account (sign up)

This offline example does not require an LLM provider key.

Launch Arize AX

Sign in to your Arize AX account.
From Space Settings, copy your Space ID and API Key.

Install

pip install arize-otel openinference-instrumentation-ag2 "ag2<1.0"

Configure credentials

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="autogen-tracing-example"

Setup tracing

# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.ag2 import AG2Instrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

AG2Instrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for legacy AutoGen API.")

Initialize the instrumentor before importing autogen.

Run legacy AutoGen API

# example.py
import json
from typing import Any

from instrumentation import tracer_provider
from autogen import ConversableAgent


def get_weather(city: str) -> str:
    return f"It is 72F and sunny in {city}."


def reply_with_weather(
    agent: ConversableAgent,
    messages: list[dict[str, Any]] | None = None,
    sender: Any = None,
    config: Any = None,
) -> tuple[bool, str]:
    _, result = agent.execute_function(
        {"name": "get_weather", "arguments": json.dumps({"city": "Portland"})},
        call_id="call-1",
    )
    return True, str(result["content"])


assistant = ConversableAgent("assistant", llm_config=False, human_input_mode="NEVER")
assistant.register_function({"get_weather": get_weather})
assistant.register_reply([ConversableAgent, None], reply_with_weather, position=0)
user = ConversableAgent("user", llm_config=False, human_input_mode="NEVER")
chat = user.initiate_chat(assistant, message="What is Portland weather?", max_turns=1, silent=True)
print("assistant:", chat.chat_history[-1]["content"])

Expected output

Arize AX tracing initialized for legacy AutoGen API.
assistant: It is 72F and sunny in Portland.

Verify in Arize AX

Open project autogen-tracing-example.
You should see AGENT spans for the chat and reply, plus a TOOL span named get_weather.

Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

Arize skill (agent)
AX CLI
SDK

Install the Arize Skills plugin and let your coding agent check for you:

npx skills add Arize-ai/arize-skills

Then prompt your agent:

Use the arize-trace skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.

Export recent spans for your project — any rows mean traces are landing:

ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
  --limit 5 --stdout | jq 'length'

A non-zero count confirms spans reached Arize AX. Run ax auth login first if you have not authenticated. See the ax spans reference.

Query the project’s spans and check that at least one came back.

import os
from arize import ArizeClient

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
resp = client.spans.list(
    project=os.environ["ARIZE_PROJECT_NAME"],
    space=os.environ["ARIZE_SPACE_ID"],
    limit=5,
)
count = len(resp.spans)
print(
    f"{count} span(s) found" if count else "No spans yet — recheck setup"
)

// Reads ARIZE_API_KEY from the environment.
import { listSpans } from "@arizeai/ax-client";

const { data: spans } = await listSpans({
  project: process.env.ARIZE_PROJECT_NAME!,
  space: process.env.ARIZE_SPACE_ID!,
  limit: 5,
});
const count = spans.length;
console.log(
  count ? `${count} span(s) found` : "No spans yet — recheck setup",
);

client, err := arize.NewClient(
    arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
)
if err != nil {
    log.Fatal(err)
}
resp, err := client.Spans.List(ctx, spans.ListRequest{
    Project: os.Getenv("ARIZE_PROJECT_NAME"),
    Space:   os.Getenv("ARIZE_SPACE_ID"),
    Limit:   5,
})
if err != nil {
    log.Fatal(err)
}
fmt.Printf("%d span(s) found\n", len(resp.Spans))

SDK span references: Python · TypeScript · Go.

More patterns

For group chats, sequential chats, structured outputs, context propagation, masking controls, and optional OpenAI LLM child spans, see the AG2 tracing guide.

OpenTelemetry

LLM Providers

Agent Frameworks

Coding Agents

Platforms

Orchestration

Evaluation

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run legacy AutoGen API

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

More patterns

Resources

OpenInference AG2 Instrumentor

AG2 repository

AutoGen AgentChat tracing

​Prerequisites

​Launch Arize AX

​Install

​Configure credentials

​Setup tracing

​Run legacy AutoGen API

​Expected output

​Verify in Arize AX

​Check from the skill, CLI, or SDK

​More patterns

​Resources

OpenInference AG2 Instrumentor

AG2 repository

AutoGen AgentChat tracing

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run legacy AutoGen API

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

More patterns

Resources