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How to integrate Linear MCP with LangChain

Connect LangChain to Linear MCP. Create a new bug for team mobile, add a comment to issue lin-123, and more using natural language, with authentication handled for you.

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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.

47 Tools12 Triggers

Managed auth

Connect Linear without auth hassles

We manage OAuth, API keys, token refresh, and scopes — you just build.

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Introduction

This guide walks you through connecting Linear to LangChain using the Composio tool router. By the end, you'll have a working Linear agent that can create and update issues, manage projects and milestones, post comments, track cycles, and search issues across teams through natural language commands.

This guide will help you understand how to give your LangChain 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.

TL;DR

Here's what you'll learn:

Get and set up your OpenAI and Composio API keys
Connect your Linear project to Composio
Create a Tool Router MCP session for Linear
Initialize an MCP client and retrieve Linear tools
Build a LangChain agent that can interact with Linear
Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

Agent Framework: Build agents that can use tools and make decisions
MCP Integration: Connect to external services through Model Context Protocol adapters
Memory Management: Maintain conversation history across interactions
Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 issues, projects, milestones, updates, comments, and teams, so your agent can search and update issues, manage projects, create milestones, publish project updates, and configure teams on your behalf.

Issue search and updates: Have your agent find issues by keywords, update their details, and remove labels that no longer apply.
Project planning: Let the agent create projects, adjust existing projects, and associate new projects with the right teams.
Milestone management: Direct your agent to create project milestones with clear names and optional target dates.
Project status updates: Instruct your agent to list, publish, and revise project updates so stakeholders stay informed.
Team and conversation management: Have your agent create or update teams, review workspace comments, and remove reactions from comments.

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 step10 STEPS

Prerequisites

Before starting this tutorial, make sure you have:

Python 3.10 or higher installed on your system
A Composio account with an API key
An OpenAI API key
Basic familiarity with Python and async programming

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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

@composio/langchain provides Composio integration for LangChain
@langchain/mcp-adapters enables MCP client connections
@langchain/core is the core agent framework
dotenv/config loads environment variables

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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 your requests to Composio's API
COMPOSIO_USER_ID identifies the user for session management
OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();

What's happening:

We're importing LangChain's MCP adapter and Composio SDK
The dotenv/config import loads environment variables from your .env file
This setup prepares the foundation for connecting LangChain with Linear functionality through MCP

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

What's happening:

We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
Creating a Composio instance that will manage our connection to Linear tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['linear']
    }
);

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Linear tools
The create method takes the user ID and specifies which toolkits should be available
The returned session.mcp.url is the MCP server URL that your agent will use
This approach allows the agent to dynamically load and use Linear tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "linear-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });

What's happening:

We're creating a MultiServerMCPClient that connects to our Linear MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available Linear tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Linear related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });

What's happening:

We initialize an empty conversationHistory list to maintain context across interactions
A readline interface is used to continuously accept user input from the command line
When a user types a message, it's added to the conversation history and sent to the agent
The agent processes the request using the invoke() method with the full conversation history
Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

What's happening:

We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Linear and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['linear']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "linear-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Linear related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Linear through Composio's Tool Router.

Key features of this implementation:

Dynamic tool loading through Composio's Tool Router
Conversation history maintenance for context-aware responses
Async Python provides clean, efficient execution of agent workflows

You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

TOOLS & TRIGGERS

Supported Tools and Triggers

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

Archive issue

Archives an existing Linear issue by its UUID or shorthand identifier, removing it from active views while preserving its data for future reference.

Archive Project

Archives an existing Linear project by its UUID, removing it from active views.

Archive Project Update

Archives a project update in Linear, hiding it from the default view while preserving its data.

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 an issue in a specified Linear team with a title and optional project, description, assignee, state, priority, cycle, due date, and other properties.

Create issue relation

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

Create a label

Creates a label for a specified Linear team, or returns an existing exact-name match found in the team's initial label results.

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.

Create Team

Creates a new team in Linear with the specified name and key.

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 files

Downloads a Linear Attachment record or a private upload embedded in issue/comment Markdown; `file_name` must include the correct extension.

Get a comment

Retrieves a single Linear comment by its unique ID.

Get current user

Gets the currently authenticated Linear user's ID, name, email, and other profile information.

Get cycles by team ID

Lists cycles for a specified Linear team ID, including progress, creation and end times, and history.

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 a Linear issue by UUID or shorthand identifier, including its details, parent, cycle, attachments, comments and subscribers.

Get Linear project

Retrieves a single Linear project by its unique identifier.

Get Project Update

Retrieves a specific project update by its unique identifier.

List comments

Lists comments from Linear workspace accessible to the authenticated user.

List issue drafts

Lists the connected user's issue drafts for review or editing.

List issues by team ID

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

Get all cycles

Lists cycles across the Linear account without filters, including team IDs and scheduled end times.

List Linear issues

Lists non-archived Linear issues with optional project and assignee filters, returning a paginated issues array and page_info.

Get labels

Retrieves paginated Linear issue labels for a specified team or across the workspace.

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

Lists visible Linear teams with their members and project IDs, with pagination for teams but not nested members or projects.

List Linear users

Lists workspace users with IDs, names, emails, and active status.

List project updates

Retrieves all project updates from the Linear workspace.

Remove label from Linear issue

Removes a specified label from a Linear issue using the issue and label IDs, and returns the updated issue and labels.

Remove reaction from comment

Tool to remove a reaction on a comment.

Resolve comment

Marks a Linear comment as resolved.

Run Query or Mutation

Execute a Linear GraphQL query or mutation to retrieve or change accessible data.

Search Linear issues

Searches Linear issues by full-text query, with pagination and an option to include archived issues.

Unarchive Project Update

Unarchive a previously archived project update in Linear.

Unresolve a comment

Unresolves a previously resolved Linear comment, marking it as requiring attention again.

Update issue

Updates an existing Linear issue by ID, requiring at least one field to modify; supplied fields overwrite existing values and labels replace the label set.

Update a comment

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

Update Project

Tool to update an existing Linear project.

Update Project Update

Tool to update an existing Linear project update post.

Update Team

Updates an existing Linear team's settings, including its name, key, cycle settings, and workflow state configuration.

Validate credential

Validates the Linear API credentials by checking if the authentication token can successfully query the current user.

Who Am I

Return the identity (name, email) of the connected Linear account.

FRAMEWORKS

How to build Linear MCP Agent with another framework

Claude Code

Use Linear MCP with Claude Code

Cursor

Use Linear MCP with Cursor

ChatGPT

Use Linear MCP with ChatGPT

Claude Cowork

Use Linear MCP with Claude Cowork

Antigravity

Use Linear MCP with Antigravity

OpenAI Agents SDK

Use Linear MCP with OpenAI Agents SDK

Claude Agent SDK

Use Linear MCP with Claude Agent SDK

Grok Bot

Use Linear MCP with Grok Bot

Muse

Use Linear MCP with Muse

Codex

Use Linear MCP with Codex

Kimi Code

Use Linear MCP with Kimi Code

Grok Build

Use Linear MCP with Grok Build

VS Code

Use Linear MCP with VS Code

OpenCode

Use Linear MCP with OpenCode

OpenClaw

Use Linear MCP with OpenClaw

Hermes

Use Linear MCP with Hermes

Atomic Agent

Use Linear MCP with Atomic Agent

CLI

Use Linear MCP with CLI

Google ADK

Use Linear MCP with Google ADK

Vercel AI SDK

Use Linear MCP with Vercel AI SDK

Mastra AI

Use Linear MCP with Mastra AI

LlamaIndex

Use Linear MCP with LlamaIndex

CrewAI

Use Linear MCP with CrewAI

Pydantic AI

Use Linear MCP with Pydantic AI

AutoGen

Use Linear MCP with AutoGen

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. LangChain 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

How to integrate Linear MCP with LangChain

Connect Linear without auth hassles

Introduction

Also integrate Linear with

TL;DR

What is LangChain?

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

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools and Triggers

How to build Linear MCP Agent with another framework

Claude Code

Cursor

ChatGPT

Claude Cowork

Antigravity

OpenAI Agents SDK

Claude Agent SDK

Grok Bot

Muse

Codex

Kimi Code

Grok Build

VS Code

OpenCode

OpenClaw

Hermes

Atomic Agent

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Pydantic AI

AutoGen

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 LangChain?

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.