How to integrate Openlayer MCP with CrewAI

This guide walks you through connecting Openlayer to CrewAI using the Composio tool router. By the end, you'll have a working Openlayer agent that can list your projects, inspect project versions, review test results, find failing tests, and check data sources through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Openlayer account through Composio's Openlayer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Openlayer is an AI quality platform for managing projects, monitoring production data, evaluating tests, and inspecting model performance. Use it to catch regressions, track production quality, and keep AI applications reliable before and after release.

10 Tools

Introduction

This guide walks you through connecting Openlayer to CrewAI using the Composio tool router. By the end, you'll have a working Openlayer agent that can list your projects, inspect project versions, review test results, find failing tests, and check data sources through natural language commands.

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

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

Also integrate Openlayer 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 Openlayer connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Openlayer
  • Build a conversational loop where your agent can execute Openlayer 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 Openlayer MCP server, and what's possible with it?

The Openlayer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Openlayer account. It provides structured and secure access to your projects, versions, monitoring data sources, tests, results, and workspace details, so your agent can review projects, inspect model versions, check monitoring status, analyze test outcomes, and track workspace usage on your behalf.

  • Project and version reviews: Have your agent list Openlayer projects, inspect their versions, and compare processing status and passing, failing, and total test counts.
  • Production monitoring checks: Let the agent find monitoring data sources and review their backend, monitoring status, record counts, and test counts.
  • Test configuration discovery: Direct your agent to list tests for a project and narrow them by category, archive state, origin, production data use, or suggested status.
  • Test result analysis: Instruct your agent to review results for project versions, monitoring data sources, or individual tests, including execution status and available generated insights.
  • Workspace usage visibility: Have your agent check workspace status, connected account membership, usage counts, and current plan limits.

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 Openlayer 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 Openlayer 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 Openlayer MCP URL
6

Create a Composio Tool Router session for Openlayer

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

url = session.mcp.url
What's happening:
  • You create a Openlayer only session through Composio
  • Composio returns an MCP HTTP URL that exposes Openlayer 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 Openlayer 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=["openlayer"],
)
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 Openlayer through Composio's Tool Router. The agent can perform Openlayer 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 Openlayer action and event your agent gets out of the box.

Get Data Source

Return one Openlayer monitoring data source, including its backend, monitoring status, record and test counts, and optional project or workspace details.

Get Project Version

Return one Openlayer project commit or version with its processing status, commit metadata, storage URI, and passing, failing, and total test counts.

Get Workspace

Return a workspace's identity, status, connected-account membership, usage counts, and plan limits.

List Data Sources

Return one page of monitoring data sources for an Openlayer project, optionally filtered by name.

List Data Source Test Results

Return one page of the latest monitoring test results for an Openlayer data source, optionally filtered by test category or execution status.

List Projects

Return one page of projects visible to the connected Openlayer workspace, optionally filtered by name or task type.

List Project Versions

Return one page of commits and versions for an Openlayer project, including processing status, commit metadata, and passing, failing, and total test counts.

List Project Version Test Results

Return one page of test results for a project version, optionally filtered by test category, archive state, or result status.

List Test Results

Return one page of results for a test, with optional project-version, data-source, and status filters and optional generated insights.

List Tests

Return one page of tests configured for an Openlayer project, with filters for category, archive state, origin version, production-data use, and suggested status.

FRAMEWORKS

How to build Openlayer MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Openlayer MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Openlayer MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Openlayer MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Openlayer MCP with Claude Cowork

Codex logo

Codex

Use Openlayer MCP with Codex

OpenClaw logo

OpenClaw

Use Openlayer MCP with OpenClaw

Hermes logo

Hermes

Use Openlayer MCP with Hermes

CLI logo

CLI

Use Openlayer MCP with CLI

Google ADK logo

Google ADK

Use Openlayer MCP with Google ADK

LangChain logo

LangChain

Use Openlayer MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Openlayer MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Openlayer MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Openlayer MCP with LlamaIndex

FAQ

Frequently asked questions

With a standalone Openlayer MCP server, the agents and LLMs can only access a fixed set of Openlayer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Openlayer 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 Openlayer tools.

Yes, absolutely. You can configure which Openlayer 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 Openlayer data and credentials are handled as safely as possible.

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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.

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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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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.

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