How to integrate Linear MCP with CrewAI

This guide walks you through connecting Linear to CrewAI using the Composio tool router. By the end, you'll have a working Linear agent that can create a new bug for team mobile, add a comment to issue lin-123, list all cycles for the design team through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Linear account through Composio's Linear MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Linear

Oauth2Api Key

Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.

32 Tools3 Triggers

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Introduction

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

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

Also integrate Linear with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex

TL;DR

Here's what you'll learn:

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

The Linear MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Linear account. It provides structured and secure access to your team's issues, projects, and workflows, so your agent can perform actions like creating issues, posting comments, managing attachments, organizing teams, and automating project tracking on your behalf.

Automated issue creation and management: Instantly create new Linear issues, update existing ones, or archive issues to keep your team’s backlog organized and up to date.
Commenting and collaboration: Post comments on issues, facilitate team discussions, and keep everyone in the loop without manual effort.
Attachment handling: Add or download attachments to and from issues, making it easy to share files or reference important documents right from Linear.
Team and cycle insights: Retrieve all teams, fetch cycles (sprints) by team ID, and get default issue parameters to help your agent contextualize and optimize planning activities.
Personalized workspace access: Identify the current user, fetch their profile information, and tailor actions or queries to individual team members for smarter automation.

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

Before starting, make sure you have:

Python 3.9 or higher
A Composio account and API key
A Linear 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 Linear 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 Linear MCP URL

Create a Composio Tool Router session for Linear

python

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

url = session.mcp.url

What's happening:

You create a Linear only session through Composio
Composio returns an MCP HTTP URL that exposes Linear 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 Linear 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=["linear"],
)
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 Linear through Composio's Tool Router. The agent can perform Linear 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 & TRIGGERS

Supported Tools and Triggers

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

Create attachment

Creates a new attachment and associates it with a specific, existing Linear issue.

Add reaction to comment

Tool to add a reaction to an existing Linear comment.

Create a comment

Creates a new comment on a specified Linear issue.

Create linear issue

Creates a new issue in a specified Linear project and team, requiring team_id and title, and allowing optional properties like description, assignee, state, priority, cycle, and due date.

Create issue relation

Create a relationship between two Linear issues using the issueRelationCreate mutation.

Create a label

Creates a new label in Linear for a specified team, used to categorize and organize issues.

Create Project

Creates a new Linear project with specified name and team associations.

Create Project Milestone

Tool to create a project milestone in Linear with a name and optional target date and sort order.

Create Project Update

Tool to create a project status update post for a Linear project.

Delete issue

Archives an existing Linear issue by its ID, which is Linear's standard way of deleting issues; the operation is idempotent.

Download issue attachments

Downloads a specific attachment from a Linear issue; the `file_name` must include the correct file extension.

Get current user

Gets the currently authenticated user's ID, name, email, and other profile information — this is the account behind the API token, which may be a bot or service account rather than a human user.

Get cycles by team ID

Retrieves all cycles for a specified Linear team ID; cycles are time-boxed work periods (like sprints).

Get create issue default params

Fetches a Linear team's default issue estimate and state, useful for pre-filling new issue forms.

Get Linear issue

Retrieves an existing Linear issue's comprehensive details, including id, identifier, title, description, timestamps, state, team, creator, attachments, comments (with user info and timestamps, use issue.

Get Linear project

Retrieves a single Linear project by its unique identifier.

List issue drafts

Tool to list issue drafts.

List issues by team ID

Tool to list all issues for a specific Linear team, scoped by team ID.

Get all cycles

Retrieves all cycles (time-boxed sprint iterations) org-wide from the Linear account; no filters applied.

List Linear issues

Lists non-archived Linear issues; if project_id is not specified, issues from all accessible projects are returned.

Get labels

Retrieves labels from Linear.

List linear projects

Retrieves all projects from the Linear account.

List Linear states

Retrieves all workflow states for a specified team in Linear, representing the stages an issue progresses through in that team's workflow.

Get teams

Retrieves all teams with their members and projects.

List Linear users

Lists all workspace users (not team-scoped) with their IDs, names, emails, and active status.

Remove label from Linear issue

Removes a specified label from an existing Linear issue using their IDs; successful even if the label isn't on the issue.

Remove reaction from comment

Tool to remove a reaction on a comment.

Run Query or Mutation

Execute any GraphQL query or mutation against Linear's API.

Search Linear issues

Search Linear issues using full-text search across identifier, title, and description.

Update issue

Updates an existing Linear issue using its `issue_id`; requires at least one other attribute for modification, and all provided entity IDs (for state, assignee, labels, etc.

Update a comment

Tool to update an existing Linear comment's body text.

Update Project

Tool to update an existing Linear project.

FRAMEWORKS

How to build Linear MCP Agent with another framework

ChatGPT

Use Linear MCP with ChatGPT

OpenAI Agents SDK

Use Linear MCP with OpenAI Agents SDK

Claude Agent SDK

Use Linear MCP with Claude Agent SDK

Claude Code

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Claude Cowork

Use Linear MCP with Claude Cowork

Codex

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Cursor

Use Linear MCP with Cursor

OpenClaw

Use Linear MCP with OpenClaw

Hermes

Use Linear MCP with Hermes

CLI

Use Linear MCP with CLI

Google ADK

Use Linear MCP with Google ADK

LangChain

Use Linear MCP with LangChain

Vercel AI SDK

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Mastra AI

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LlamaIndex

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OpenCode

Use Linear MCP with OpenCode

VS Code

Use Linear MCP with VS Code

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Google Sheets

Oauth2

Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.

Notion

Oauth2Api Key

Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.

Airtable

Oauth2Api Key

Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.

Asana

Oauth2

Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.

FAQ

Frequently asked questions

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

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

Start with Linear.It takes 30 seconds.

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

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

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.

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

Co-Founder, Ingram Technologies

How to integrate Linear MCP with CrewAI

Connect Linear without auth hassles

Introduction

Also integrate Linear with

TL;DR

What is CrewAI?

What is the Linear 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

Create a Composio Tool Router session for Linear

Initialize the MCP Server

Create a CLI Chatloop and define the Crew

Complete Code

Conclusion

Supported Tools and Triggers

How to build Linear MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

OpenCode

VS Code

Explore Other Toolkits

Google Sheets

Notion

Airtable

Asana

Frequently asked questions

What are the differences in Tool Router MCP and Linear MCP?+

Can I use Tool Router MCP with CrewAI?+

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

How safe is my data with Composio Tool Router?+

Start with Linear.It takes 30 seconds.

Teams ship real agents on Composio