Microsoft AutoGen Integration with Veritrix Tracing

Veritrix brings full observability to Microsoft AutoGen’s multi-agent conversation framework with a single initialization call. Once you call trace.init(), Veritrix automatically instruments every agent conversation turn, LLM invocation, tool execution, and reply chain across your entire AutoGen pipeline — so you can diagnose runaway loops, measure per-agent costs, and understand exactly how your agents collaborate to reach a final answer.

Prerequisites

Python 3.10 or higher
An active Veritrix account and API key
AutoGen installed (pyautogen or the newer autogen-agentchat package)

Installation & Setup

Install Veritrix and AutoGen

pip install veritrix pyautogen

If you are using the newer AutoGen AgentChat API, install autogen-agentchat instead:

pip install veritrix autogen-agentchat

Initialize Veritrix tracing

Call trace.init() once at application startup, before you define any agents or start any conversations.

from veritrix import trace

trace.init("autogen-pipeline")

Use AutoGen as normal

Veritrix patches AutoGen’s conversation runtime automatically. Your agent definitions and conversation logic remain unchanged.

import autogen
from veritrix import trace

trace.init("autogen-pipeline")
# Your AutoGen agents run normally

Example: Two-Agent Conversation

The following example sets up a classic AutoGen pattern — an assistant agent and a user proxy — to collaboratively solve a coding problem. Veritrix traces every message exchange and LLM call in the conversation.

import autogen
from veritrix import trace

trace.init("autogen-pipeline")

config_list = [{"model": "gpt-4o", "api_key": "YOUR_OPENAI_API_KEY"}]

assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list},
    system_message="You are a helpful AI assistant that writes clean Python code.",
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=5,
    code_execution_config={"work_dir": "coding", "use_docker": False},
)

user_proxy.initiate_chat(
    assistant,
    message="Write a Python function that returns the nth Fibonacci number using memoization.",
)

Example: Group Chat with Multiple Agents

For GroupChat pipelines, Veritrix traces each agent’s contribution and the manager’s routing decisions.

import autogen
from veritrix import trace

trace.init("groupchat-pipeline")

config_list = [{"model": "gpt-4o", "api_key": "YOUR_OPENAI_API_KEY"}]
llm_config = {"config_list": config_list}

planner = autogen.AssistantAgent(name="planner", llm_config=llm_config,
    system_message="You break problems into clear subtasks.")

executor = autogen.AssistantAgent(name="executor", llm_config=llm_config,
    system_message="You implement solutions for the subtasks the planner defines.")

critic = autogen.AssistantAgent(name="critic", llm_config=llm_config,
    system_message="You review solutions and suggest improvements.")

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=1,
)

groupchat = autogen.GroupChat(
    agents=[planner, executor, critic, user_proxy],
    messages=[],
    max_round=6,
)

manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

user_proxy.initiate_chat(manager, message="Build a REST API endpoint that returns weather data.")

What Gets Captured Automatically

Signal	Details
LLM calls	Model name, messages, completion, token counts, latency per agent
Tool / function calls	Function name, arguments, return value, duration
Conversation turns	Speaker, message content, reply chain depth
Agent handoffs	Routing decisions in GroupChat, receiving agent
Code execution	Code block submitted, stdout/stderr, exit code
Retries	Retry count, error type, consecutive auto-reply counter

Veritrix captures the full conversation history attached to each trace, making it easy to replay and audit long multi-agent exchanges directly from the dashboard.

OpenTelemetry Compatibility

Veritrix is built natively on OpenTelemetry. Every span generated for your AutoGen pipelines is a standard OTel span that can be exported to any OTel-compatible collector — Jaeger, Honeycomb, Grafana Tempo, or your own backend — without any extra configuration.

To route traces to a custom OTel exporter or an existing observability stack, see the OpenTelemetry integration page.

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Microsoft AutoGen Integration with Veritrix Tracing

Prerequisites

Installation & Setup

Example: Two-Agent Conversation

Example: Group Chat with Multiple Agents

What Gets Captured Automatically

OpenTelemetry Compatibility

​Prerequisites

​Installation & Setup

​Example: Two-Agent Conversation

​Example: Group Chat with Multiple Agents

​What Gets Captured Automatically

​OpenTelemetry Compatibility

Prerequisites

Installation & Setup

Example: Two-Agent Conversation

Example: Group Chat with Multiple Agents

What Gets Captured Automatically

OpenTelemetry Compatibility