How to integrate Gitlab MCP with CrewAI

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Introduction

This guide walks you through connecting Gitlab to CrewAI using the Composio tool router. By the end, you'll have a working Gitlab agent that can create new gitlab group for qa team, open bug issue in frontend project, create branch from latest main commit, archive completed api migration project through natural language commands.

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

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

TL;DR

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

The Gitlab MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Gitlab account. It provides structured and secure access to your repositories, projects, and issues, so your agent can perform actions like creating projects, managing issues, handling branches, and automating DevOps workflows on your behalf.

  • Project and group automation: Instantly create new Gitlab projects or organize your workspaces by setting up project groups—all without manual clicks.
  • Issue creation and tracking: Have your agent report bugs, request features, or open new issues in specific projects to keep your team on top of tasks.
  • Branch management: Let your agent create repository branches from any commit or base branch, making it easy to streamline your development process.
  • Project lifecycle management: Archive completed projects or delete unneeded ones, keeping your workspace clean and up to date with minimal effort.
  • Commit and job insights: Retrieve commit references, determine commit sequence in project history, or erase job artifacts and logs for deeper CI/CD control.

Supported Tools & Triggers

Tools
Archive ProjectTool to archive a project.
Create GitLab GroupTool to create a new group in gitlab.
Create ProjectTool to create a new project in gitlab.
Create Project IssueTool to create a new issue in a gitlab project.
Create Repository BranchTool to create a new branch in a project.
Delete ProjectTool to delete a gitlab project by its id.
Download Project AvatarTool to download a project’s avatar image.
Erase JobTool to erase the content of a specified job within a project.
Get Commit ReferencesTool to get all references (branches or tags) a commit is pushed to.
Get Commit SequenceTool to get the sequence number of a commit in a project by following parent links from the given commit.
Get Group DetailsTool to retrieve information about a specific group by its id.
Get Group MemberTool to retrieve details for a specific group member.
Get GroupsGet groups
Get Job DetailsTool to retrieve details of a single job by its id within a specified project.
Get Merge Request NotesTool to fetch comments on a merge request.
Get ProjectTool to get a single project by id or url-encoded path.
Get Project LanguagesTool to list programming languages used in a project with percentages.
Get Project MemberTool to retrieve details for a specific project member.
Get Project Member AllTool to retrieve details for a specific project member (including inherited and invited members).
Get Merge Request CommitsTool to get commits of a merge request.
Get Project Merge RequestsTool to retrieve a list of merge requests for a specific project.
Get ProjectsTool to list all projects accessible to the authenticated user.
List Merge Request DiffsTool to list all diff versions of a merge request.
Get Repository BranchTool to retrieve information about a specific branch in a project.
Get Repository BranchesRetrieves a list of repository branches for a project.
Get Single CommitTool to get a specific commit identified by the commit hash or name of a branch or tag.
Get Single PipelineTool to retrieve details of a single pipeline by its id within a specified project.
Get UserTool to retrieve information about a specific user by their id.
Get User PreferencesTool to get the current user's preferences.
Get UsersTool to retrieve a list of users from gitlab.
Get User StatusTool to get a user's status by id.
Get User StatusTool to get the current user's status.
Get User Support PINTool to get details of the current user's support pin.
Import project membersTool to import members from one project to another.
List All Group MembersTool to list all members of a group including direct, inherited, and invited members.
List All Project MembersTool to list all members of a project (direct, inherited, invited).
List Billable Group MembersTool to list billable members of a top-level group (including its subgroups and projects).
List Group MembersTool to list direct members of a group.
List Pending Group MembersTool to list pending members of a group and its subgroups and projects.
List Pipeline JobsTool to retrieve a list of jobs for a specified pipeline within a project.
List Project GroupsTool to list ancestor groups of a project.
List Project Invited GroupsTool to list groups invited to a project.
List Project PipelinesTool to retrieve a list of pipelines for a specified project.
List Project Shareable GroupsTool to list groups that can be shared with a project.
List Project Repository TagsTool to retrieve a list of repository tags for a specified project.
List Project Transfer LocationsTool to list namespaces available for project transfer.
List project usersTool to list users of a project.
List Repository CommitsTool to get a list of repository commits in a project.
List User ProjectsTool to list projects owned by a specific user.
Create Support PINTool to create a support pin for your authenticated user.
Update User PreferencesTool to update the current user's preferences.
Set User StatusTool to set the current user's status.
Share Project With GroupTool to share a project with a group.
Start Housekeeping TaskTool to start the housekeeping task for a project.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

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

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

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

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 Gitlab MCP URL

Create a Composio Tool Router session for Gitlab

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

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

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.

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 Gitlab and CrewAI:

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=["gitlab"],
)
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 Gitlab through Composio's Tool Router. The agent can perform Gitlab 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

How to build Gitlab MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Gitlab MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Gitlab MCP with Claude Agent SDK

Google ADK

Google ADK

Use Gitlab MCP with Google ADK

LangChain

LangChain

Use Gitlab MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Gitlab MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Gitlab MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Gitlab MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Gitlab MCP?

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

Can I use Tool Router MCP with CrewAI?

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 Gitlab tools.

Can I manage the permissions and scopes for Gitlab while using Tool Router?

Yes, absolutely. You can configure which Gitlab 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.

How safe is my data with Composio Tool Router?

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 Gitlab data and credentials are handled as safely as possible.

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