How to integrate Arize AX MCP with Autogen

This guide walks you through connecting Arize AX to AutoGen using the Composio tool router. By the end, you'll have a working Arize AX agent that can summarize failed spans from latest traces, compare experiment results across prompt versions, create dataset from production traces through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Arize AX account through Composio's Arize AX MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Arize AX

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Arize AX is an AI engineering platform for tracing, evaluating, and improving AI applications. Use it to debug LLM behavior, compare experiments, and improve production AI quality.

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Introduction

This guide will help you understand how to give your AutoGen agent real control over a Arize AX account through Composio's Arize AX MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Arize AX with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

Get and set up your OpenAI and Composio API keys
Install the required dependencies for Autogen and Composio
Initialize Composio and create a Tool Router session for Arize AX
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call Arize AX tools
Run a live chat loop where you ask the agent to perform Arize AX operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

Multi-Agent Systems: Build collaborative agent workflows
MCP Workbench: Native support for Model Context Protocol tools
Streaming HTTP: Connect to external services through streamable HTTP
AssistantAgent: Pre-built agent class for tool-using assistants

What is the Arize AX MCP server, and what's possible with it?

The Arize AX MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Arize AX account. It provides structured and secure access to your projects, datasets, prompts, experiments, traces, and spans, so your agent can inspect AI activity, manage evaluation data, maintain prompts, run experiments, and review results on your behalf.

Project and space discovery: Have your agent find accessible spaces and projects, then retrieve details about the AI applications you monitor.
Dataset management: Let the agent create and rename datasets, add or update examples, and review examples from current or named dataset versions.
Prompt versioning: Direct your agent to create prompts, publish new immutable versions, update prompt descriptions, and review current or previous versions.
Experiment tracking: Instruct your agent to create experiments, append runs, and inspect experiment outputs for evaluation and comparison.
Trace and span analysis: Have your agent review bounded sets of traces and spans within chosen time ranges and filters to investigate application behavior.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS

Prerequisites

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A Arize AX account you can connect to Composio
Some basic familiarity with Autogen and Python async

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
Keep the API key safe.

Composio API Key

Log in to the Composio dashboard.
Navigate to your API settings and generate a new API key.
Store this key securely as you'll need it for authentication.

Install dependencies

bash

pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

composio connects your agent to Arize AX via MCP
autogen-agentchat provides the AssistantAgent class
autogen-ext-openai provides the OpenAI model client
autogen-ext-tools provides MCP workbench support

Set up environment variables

bash

COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

COMPOSIO_API_KEY is required to talk to Composio
OPENAI_API_KEY is used by Autogen's OpenAI client
USER_ID is how Composio identifies which user's Arize AX connections to use

Import dependencies and create Tool Router session

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Arize AX session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["arize_ax"]
    )
    url = session.mcp.url

What's happening:

load_dotenv() reads your .env file
Composio(api_key=...) initializes the SDK
create(...) creates a Tool Router session that exposes Arize AX tools
session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python

# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

url points to the Tool Router MCP endpoint from Composio
timeout is the HTTP timeout for requests
sse_read_timeout controls how long to wait when streaming responses
terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python

# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Arize AX assistant agent with MCP tools
    agent = AssistantAgent(
        name="arize_ax_assistant",
        description="An AI assistant that helps with Arize AX operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

OpenAIChatCompletionClient wraps the OpenAI model for Autogen
McpWorkbench connects the agent to the MCP tools
AssistantAgent is configured with the Arize AX tools from the workbench

Run the interactive chat loop

python

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Arize AX related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

What's happening:

The script prompts you in a loop with You:
Autogen passes your input to the model, which decides which Arize AX tools to call via MCP
agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Arize AX and AutoGen:

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Arize AX session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["arize_ax"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Arize AX assistant agent with MCP tools
        agent = AssistantAgent(
            name="arize_ax_assistant",
            description="An AI assistant that helps with Arize AX operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Arize AX related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Arize AX through Composio's Tool Router and MCP. From here you can:

Add more toolkits to the toolkits list, for example notion or hubspot
Refine the agent description to point it at specific workflows
Wrap this script behind a UI, Slack bot, or internal tool

Once the pattern is clear for Arize AX, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

TOOLS

Supported Tools

Every Arize AX action and event your agent gets out of the box.

Add Dataset Examples

Append arbitrary-field examples to an Arize dataset or a specified dataset version.

Add Experiment Runs

Append one or more runs to an existing Arize experiment.

Create Dataset

Create an Arize dataset in a space with an explicit initial array of examples.

Create Experiment

Create an Arize experiment with initial runs, associated with exactly one dataset or space.

Create Prompt

Create an Arize prompt and its initial version in a space.

Create Prompt Version

Create a new immutable version of an existing Arize prompt.

Get Dataset

Get one Arize dataset and its versions by dataset ID.

Get Experiment

Get one Arize experiment by its ID, without its experiment runs.

Get Project

Get one accessible Arize project by its ID.

Get Prompt

Get an Arize prompt with its current, explicitly selected, or labeled version.

Get Space

Get one accessible Arize space by its ID.

List Dataset Examples

List one page of examples from an Arize dataset or a specific dataset version, preserving user-defined example fields.

List Datasets

List one page of Arize datasets, optionally filtered by space or dataset name.

List Experiment Runs

List one page of runs and outputs for an Arize experiment, preserving additional run fields.

List Experiments

List one page of Arize experiments, optionally filtered by dataset, space, or name.

List Projects

List one page of accessible Arize projects, optionally filtered by space or project name.

List Prompts

List one page of Arize prompts, optionally filtered by space or prompt name.

List Prompt Versions

List one page of versions for an Arize prompt, newest first.

List Spaces

List one page of Arize spaces accessible to the connected API key, optionally filtered by organization or name.

List Spans

Read one bounded page of spans for an Arize project using optional time and filter constraints.

List Traces

Read one bounded page of traces and nested spans for an Arize project using optional time and filter constraints; nested spans may be truncated per trace.

Update Dataset

Rename an existing Arize dataset.

Update Dataset Examples

Update existing Arize dataset examples by ID, optionally creating a named dataset version.

Update Prompt

Update or clear an Arize prompt's description.

FRAMEWORKS

How to build Arize AX MCP Agent with another framework

OpenAI Agents SDK

Use Arize AX MCP with OpenAI Agents SDK

Claude Agent SDK

Use Arize AX MCP with Claude Agent SDK

Claude Code

Use Arize AX MCP with Claude Code

Claude Cowork

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Codex

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OpenClaw

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Hermes

Use Arize AX MCP with Hermes

CLI

Use Arize AX MCP with CLI

Google ADK

Use Arize AX MCP with Google ADK

LangChain

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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CrewAI

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FAQ

Frequently asked questions

With a standalone Arize AX MCP server, the agents and LLMs can only access a fixed set of Arize AX tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Arize AX and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Arize AX tools.

Yes, absolutely. You can configure which Arize AX scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Arize AX data and credentials are handled as safely as possible.

Start with Arize AX.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Arize AX tool your agent needs.Free to start.

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Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Arize AX MCP with Autogen

Connect Arize AX without auth hassles

Introduction

Also integrate Arize AX with

TL;DR

What is AutoGen?

What is the Arize AX MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

Supported Tools

How to build Arize AX MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

AltTextLab

Apiframe

AssemblyAI

Avoma

Frequently asked questions

What are the differences in Tool Router MCP and Arize AX MCP?+

Can I use Tool Router MCP with Autogen?+

Can I manage the permissions and scopes for Arize AX while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Arize AX.It takes 30 seconds.

Teams ship real agents on Composio