How to integrate Arize AX MCP with CrewAI

This guide walks you through connecting Arize AX to CrewAI 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 CrewAI 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.

Get started for freeGet a demo
Arize AX logoArize AX
Api Key

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.

24 Tools

Introduction

This guide walks you through connecting Arize AX to CrewAI 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 CrewAI 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 SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Arize AX connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Arize AX
  • Build a conversational loop where your agent can execute Arize AX operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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:

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

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Arize AX connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

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.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Arize AX via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Arize AX MCP URL
6

Create a Composio Tool Router session for Arize AX

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["arize_ax"])

url = session.mcp.url
What's happening:
  • You create a Arize AX only session through Composio
  • Composio returns an MCP HTTP URL that exposes Arize AX tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["arize_ax"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Arize AX through Composio's Tool Router. The agent can perform Arize AX operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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 logo

OpenAI Agents SDK

Use Arize AX MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Arize AX MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Arize AX MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Arize AX MCP with Claude Cowork

Codex logo

Codex

Use Arize AX MCP with Codex

OpenClaw logo

OpenClaw

Use Arize AX MCP with OpenClaw

Hermes logo

Hermes

Use Arize AX MCP with Hermes

CLI logo

CLI

Use Arize AX MCP with CLI

Google ADK logo

Google ADK

Use Arize AX MCP with Google ADK

LangChain logo

LangChain

Use Arize AX MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Arize AX MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Arize AX MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Arize AX MCP with LlamaIndex

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. CrewAI 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.

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

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
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
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
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
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.

Tomisin Jenrola
Tomisin Jenrola
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
Jerome Leclanche
Co-Founder, Ingram Technologies