How to integrate Circleci MCP with CrewAI

This guide walks you through connecting Circleci to CrewAI using the Composio tool router. By the end, you'll have a working Circleci agent that can trigger a new pipeline on main branch, list all pipelines for backend service, get test results from last successful build through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Circleci account through Composio's Circleci MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Circleci is a leading continuous integration and delivery platform for automating code builds, tests, and deployments. It helps teams ship quality software faster by streamlining DevOps workflows.

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Introduction

This guide walks you through connecting Circleci to CrewAI using the Composio tool router. By the end, you'll have a working Circleci agent that can trigger a new pipeline on main branch, list all pipelines for backend service, get test results from last successful build through natural language commands.

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

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

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

The Circleci MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Circleci account. It provides structured and secure access to your Circleci projects and pipelines, so your agent can trigger builds, fetch job artifacts, monitor workflows, and analyze test results on your behalf.

  • Automated pipeline triggering and management: Let your agent start new builds for specific branches or tags, enabling continuous integration workflows without manual intervention.
  • Workflow and job status monitoring: Ask your agent to fetch detailed information about jobs and workflows, including status, timing, and execution environment, to stay on top of your CI/CD processes.
  • Artifact and test result retrieval: Have the agent collect job artifacts or extract comprehensive test metadata and failure messages for easier debugging and reporting.
  • Pipeline and runner insights: Get your agent to list all pipelines for a project or enumerate available self-hosted runners, making it simple to manage and audit your Circleci resources.
  • User and configuration access: Retrieve user profile details or fetch pipeline YAML configurations as needed for documentation, troubleshooting, or workflow optimization.

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 Circleci 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 Circleci 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 Circleci MCP URL
6

Create a Composio Tool Router session for Circleci

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

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

Create Context

Tool to create a new context in CircleCI.

Create Context (GraphQL)

Tool to create a new CircleCI context using the GraphQL API.

Create Context Restriction

Tool to create a context restriction in CircleCI.

Create Organization Orb Allowlist

Tool to create a new URL Orb allow-list entry for an organization.

Create Organization Project

Tool to create a new project within a CircleCI organization.

Create Organization Group

Tool to create a group in an organization.

Create Project Environment Variable

Tool to create a new environment variable for a CircleCI project.

Create Usage Export Job

Tool to create a usage export job for a CircleCI organization.

Delete Context (GraphQL)

Tool to delete a CircleCI context by its UUID using GraphQL API.

Delete Context Restriction

Tool to delete a context restriction by its ID.

Delete Namespace and Related Orbs

Tool to delete a CircleCI registry namespace and all its associated orbs.

Delete Namespace Alias

Tool to remove a namespace alias by name in CircleCI.

Delete Organization Orb Allowlist Entry

Tool to remove an entry from the organization's URL orb allow-list.

Delete Organization Group

Tool to delete a group from a CircleCI organization.

Delete Project

Tool to delete a CircleCI project and its settings.

Delete Project Environment Variable

Tool to delete an environment variable from a CircleCI project.

Get Context

Tool to retrieve a context by its unique ID.

Get Current User

Tool to retrieve information about the currently authenticated user.

Get Flaky Tests

Tool to get flaky tests for a project.

Get Job Artifacts

Retrieves artifacts (output files like test results, logs, build binaries, reports) produced by a CircleCI job.

Get Job Details

Tool to fetch details of a specific job within a project.

Get Orb Details

Tool to query detailed information about a CircleCI orb using the GraphQL API.

Get Orb Version

Tool to retrieve detailed information about a specific CircleCI orb version via GraphQL.

Get Organization

Tool to retrieve organization details from CircleCI using GraphQL query.

Get Organization Group

Tool to retrieve a group in an organization.

Get Pipeline Config

Tool to fetch pipeline configuration by ID.

Get Pipeline Definition

Tool to retrieve a pipeline definition by project and definition ID.

Get Project

Tool to retrieve a CircleCI project by its slug.

Get Project Workflows

Tool to get summary metrics for all workflows of a project.

Get Test Metadata

Tool to fetch test metadata for a specific job.

Get Usage Export Job

Tool to retrieve a usage export job by organization ID and job ID.

Get User Information

Tool to retrieve information about a CircleCI user by their unique ID.

Get Workflow Summary

Tool to get metrics and trends for a workflow.

List Context Environment Variables

Tool to list all environment variables for a specific context.

List Insights Branches

Tool to get all branches for a project from CircleCI Insights.

List Insights Summary

Tool to get summary metrics with trends for the entire organization and for each project.

List Namespace Orbs

Tool to list orbs in a CircleCI registry namespace with pagination support.

List Orb Categories

Tool to retrieve all CircleCI orb categories with pagination support.

List Orbs

Tool to list CircleCI orbs with pagination support via GraphQL API.

List Organization Groups

Tool to list all groups in a CircleCI organization.

List Pages Summary

Tool to get summary metrics and trends for a project across its workflows and branches.

List Pipeline Definitions

Tool to list all pipeline definitions for a specific project.

List Pipelines

Tool to get a list of pipelines for an organization.

List Pipelines for Project

Tool to list all pipelines for a specific project.

List Project Environment Variables

Tool to list all environment variables for a CircleCI project.

List Project Schedules

Tool to list all schedules for a specific project.

List Self-Hosted Runners

List self-hosted runners in CircleCI.

List User Collaborations

Tool to retrieve organizations where the authenticated user has access.

List Workflows by Pipeline ID

Tool to list all workflows associated with a specific pipeline.

List Workflows Jobs Workflows

Tool to get summary metrics for a project workflow's jobs.

List Workflows Test Metrics

Tool to get test metrics for a project's workflows.

Query Context

Tool to retrieve a CircleCI context by its UUID using GraphQL API.

Query Namespace Exists

Tool to determine if a namespace exists in the CircleCI registry.

Query Orb Category ID

Tool to fetch the unique category ID for a CircleCI orb category by its name.

Query Orb Exists

Tool to check if an orb exists in CircleCI registry and retrieve its privacy status.

Query Orb ID

Tool to fetch an orb's ID and optionally its namespace ID by orb name.

Query Orb Latest Version

Tool to fetch the latest published version of a CircleCI orb.

Query Orb Source

Tool to retrieve source code of a specific CircleCI orb version via GraphQL.

Query Plan Metrics

Tool to query plan metrics including credit usage by project and organization for a date range.

Remove Context Environment Variable (GraphQL)

Tool to remove an environment variable from a CircleCI context using GraphQL API.

Rename Namespace

Tool to rename a CircleCI namespace by its UUID identifier.

Store Environment Variable

Tool to store an environment variable in a CircleCI context using GraphQL mutation.

Trigger Pipeline

Triggers a new CI/CD pipeline run for a specified CircleCI project.

Upsert Context Environment Variable

Tool to add or update an environment variable in a CircleCI context.

Validate Orb Config

Tool to validate CircleCI orb YAML configuration using the orbConfig GraphQL query.

FRAMEWORKS

How to build Circleci MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Circleci MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Circleci MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Circleci MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Circleci MCP with Claude Cowork

Codex logo

Codex

Use Circleci MCP with Codex

OpenClaw logo

OpenClaw

Use Circleci MCP with OpenClaw

Hermes logo

Hermes

Use Circleci MCP with Hermes

CLI logo

CLI

Use Circleci MCP with CLI

Google ADK logo

Google ADK

Use Circleci MCP with Google ADK

LangChain logo

LangChain

Use Circleci MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Circleci MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Circleci MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Circleci MCP with LlamaIndex

FAQ

Frequently asked questions

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

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

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