How to integrate Todoist MCP with LangChain

This guide walks you through connecting Todoist to LangChain using the Composio tool router. By the end, you'll have a working Todoist agent that can add a high-priority task for today, create a new project called 'team offsite', close all completed tasks from this week through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Todoist account through Composio's Todoist MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Todoist is a task management app for to-do lists, projects, and reminders. Stay organized and on track with easy deadlines, collaboration, and cross-platform syncing.

76 Tools1 Triggers

Introduction

This guide walks you through connecting Todoist to LangChain using the Composio tool router. By the end, you'll have a working Todoist agent that can add a high-priority task for today, create a new project called 'team offsite', close all completed tasks from this week through natural language commands.

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

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

Also integrate Todoist with

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Todoist project to Composio
  • Create a Tool Router MCP session for Todoist
  • Initialize an MCP client and retrieve Todoist tools
  • Build a LangChain agent that can interact with Todoist
  • 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 Todoist MCP server, and what's possible with it?

The Todoist MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Todoist account. It provides structured and secure access to your tasks, projects, and labels, so your agent can create tasks, manage projects, add comments, organize sections, and update your to-do lists on your behalf.

  • Task creation and scheduling: Instantly ask your agent to add new tasks with specific details, deadlines, priorities, or even as subtasks within projects or sections.
  • Project and workspace management: Let your agent create, organize, or delete projects and workspaces to keep your productivity system tidy and up-to-date.
  • Section and label organization: Direct your agent to create, delete, or update sections and labels, helping you structure your tasks and filter lists for better focus.
  • Task completion and commenting: Have your agent mark tasks as complete or add helpful comments and notes to specific tasks or projects for seamless collaboration.
  • Streamlined cleanup and maintenance: Empower your agent to remove unused projects, labels, or sections, ensuring your Todoist stays clutter-free and organized.

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 step10 STEPS
1

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
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

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
4

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
5

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 Todoist functionality through MCP
6

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 Todoist tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

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

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Todoist 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 Todoist tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "todoist-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 Todoist MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Todoist tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

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 Todoist 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
10

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 Todoist 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: ['todoist']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "todoist-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 Todoist 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 Todoist 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 Todoist action and event your agent gets out of the box.

Add Workspace

Tool to create a new workspace in Todoist.

Archive Project (API v1)

Tool to archive a project using Todoist API v1.

Bulk Create Tasks

Create many tasks in one request using Todoist's Sync batching.

Close Task (API v1)

Tool to close (complete) a task in Todoist using API v1.

Create Comment (API v1)

Tool to create a new comment on a project or task using Todoist API v1.

Create Label (API v1)

Tool to create a new personal label using API v1.

Create Project (API v1)

Tool to create a new project in Todoist using the unified API v1.

Create Section (API v1)

Tool to create a new section within a project using API v1.

Create task

Create a new task in Todoist using the unified API v1.

Delete Comment

Tool to delete a specific comment from Todoist by its ID.

Delete Label (V1)

Tool to delete a personal label using API v1.

Delete Project (API v1)

Tool to delete a project and all of its sections and tasks using Todoist API v1.

Delete Section (v1)

Tool to delete a section and all tasks within it.

Delete Task

Tool to delete a specific task from Todoist.

Delete Upload

Tool to delete an uploaded file from Todoist.

Export Template As File

Tool to export a Todoist project as a CSV template file.

Export Template As URL

Tool to export a Todoist project as a shareable template URL.

Filter Tasks

Tool to get all tasks matching the filter.

Get All Comments

This tool retrieves all comments associated with a specific task or project in Todoist.

Get all projects

Get all projects from a user's Todoist account.

Get All Tasks

Fetches all INCOMPLETE tasks from Todoist and returns their details.

Get Backups

Tool to list all available backup archives for the user.

Get Comment (V1)

Tool to retrieve a single comment by ID using the v1 API.

Get Completed Tasks By Completion Date

Tool to retrieve completed tasks within a specified completion date window.

Get ID Mappings

Tool to translate IDs between Todoist API v1 and v2.

Get Personal Label

Tool to retrieve a personal label by its ID.

Get Productivity Stats

Tool to retrieve comprehensive productivity statistics for the authenticated user.

Get Project (API v1)

Tool to retrieve a specific project by its ID using Todoist API v1.

Get Full Project Data

Tool to retrieve full project data including all sections, tasks, and collaborators.

Get Project Permissions

Tool to retrieve all available roles and their associated actions in Todoist projects.

Get Section (v1 API)

Tool to retrieve a specific section by its ID using Todoist v1 API.

Get Special Backups

Tool to list special backup archives for the authenticated user's projects.

Get Task (API v1)

Tool to retrieve a single active (non-completed) task by ID using API v1.

Get User

Tool to retrieve information about the currently authenticated user.

Get Workspace Plan Details

Tool to retrieve details about a workspace's current plan and usage.

Import Template Into Project By ID

Tool to import a template from Todoist's template gallery into an existing project.

Import Template Into Project From File

Tool to import a CSV template into an existing Todoist project from a file.

Invite Project Collaborator

Tool to invite a collaborator to a Todoist project by email.

List Activities

Tool to get activity logs from Todoist.

List All Workspace Invitations

Tool to return a list containing details of all pending invitations to a workspace.

List Archived Projects

Tool to get all archived projects from Todoist.

List Archived Sections

Tool to retrieve all archived sections for a specific project in Todoist.

List Archived Workspace Projects

Tool to list all archived projects in a workspace.

List Completed Tasks

Tool to retrieve all completed tasks with optional project filtering.

List Completed Tasks By Due Date

Tool to retrieve completed tasks within a specified due date range (up to 6 weeks).

List Filters

Tool to list all filters for the authenticated user.

List Joinable Workspaces

Tool to get workspaces the user can join.

List Labels

Tool to get all user labels with pagination support.

List Pending Workspace Invitations

Tool to list pending invitation emails in a workspace.

List Project Collaborators

Tool to get all collaborators for a given project with cursor-based pagination.

List Sections

Tool to get all active sections for the user, with optional filtering by project.

List Shared Labels

Tool to retrieve shared label names from active tasks with pagination support.

List Workspace Active Projects

Tool to list all active workspace projects.

List Workspace Archived Projects

Tool to get archived projects in a workspace.

List Workspace Invitations

Tool to list user emails with pending invitations to a workspace.

List Workspace Users

Tool to list users in workspace(s).

Move Task

Tool to move a task to another project, section, or parent task while preserving task identity and metadata.

Move Task (REST API)

Tool to move a task to another project, section, or parent task using the REST API.

Quick Add Task

Tool to add tasks using natural language parsing similar to the official Todoist clients.

Remove Shared Label (API v1)

Tool to remove a shared label from all active tasks using API v1.

Rename Shared Labels (API v1)

Tool to rename a shared label across all active tasks using API v1.

Reopen Task (API v1)

Tool to reopen a completed task in Todoist using API v1.

Reorder Tasks

Reorder tasks deterministically by updating child_order in bulk via the Sync API item_reorder command.

Search Labels

Tool to search user labels by name with case-insensitive matching.

Search Projects

Search active user projects by name with support for wildcards and pagination.

Search Sections

Tool to search active sections by name, optionally filtered by project.

Todoist Sync

Tool to sync data with Todoist server, supporting both read and write operations.

Unarchive Project (API v1)

Tool to unarchive a previously archived Todoist project using API v1.

Update Comment (v1)

Tool to update a comment by ID and return its content via v1 API.

Update Label (API v1)

Tool to update an existing label using API v1.

Update Notification Setting

Tool to update notification settings for the current user.

Update Project (API v1)

Tool to update a project's properties using Todoist API v1.

Update Section (v1)

Tool to update an existing section by its ID using Todoist v1 API.

Update Task

Tool to update an existing task's properties.

Update Workspace Logo

Tool to upload an image as the workspace logo or delete the existing logo.

Upload File

Tool to upload a file to Todoist.

FRAMEWORKS

How to build Todoist MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Todoist MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Todoist MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Todoist MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Todoist MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Todoist MCP with Claude Cowork

Codex logo

Codex

Use Todoist MCP with Codex

Cursor logo

Cursor

Use Todoist MCP with Cursor

OpenClaw logo

OpenClaw

Use Todoist MCP with OpenClaw

Hermes logo

Hermes

Use Todoist MCP with Hermes

CLI logo

CLI

Use Todoist MCP with CLI

Google ADK logo

Google ADK

Use Todoist MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Todoist MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Todoist MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Todoist MCP with LlamaIndex

CrewAI logo

CrewAI

Use Todoist MCP with CrewAI

OpenCode logo

OpenCode

Use Todoist MCP with OpenCode

VS Code logo

VS Code

Use Todoist MCP with VS Code

FAQ

Frequently asked questions

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

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

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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
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
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
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
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.

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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.

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Co-Founder, Ingram Technologies