How to integrate Checkvist MCP with LangChain

This guide walks you through connecting Checkvist to LangChain using the Composio tool router. By the end, you'll have a working Checkvist agent that can create your checklists, add tasks, search tasks, import task lists, and view task outlines through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Checkvist account through Composio's Checkvist MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Checkvist is an online outliner and task manager for hierarchical lists, notes, tags, due dates, and assignees. Use it to plan projects, capture ideas, and organize work in clean nested lists.

16 Tools

Introduction

This guide walks you through connecting Checkvist to LangChain using the Composio tool router. By the end, you'll have a working Checkvist agent that can create your checklists, add tasks, search tasks, import task lists, and view task outlines through natural language commands.

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

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

Also integrate Checkvist with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Checkvist MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Checkvist account. It provides structured and secure access to your checklists, tasks, tags, due dates, assignees, and task notes, so your agent can create lists, organize task hierarchies, search tasks, update assignments, and track task status on your behalf.

  • Checklist creation and organization: Have your agent create, rename, tag, review, or delete checklists, including archived and private lists.
  • Hierarchical task planning: Let the agent create individual tasks or import complete task hierarchies, then arrange subtasks, priorities, positions, due dates, tags, and assignees.
  • Task search and review: Instruct your agent to search across checklists by keywords, due dates, tags, status, or notes, and read checklist outlines or task trees.
  • Status and detail updates: Direct your agent to close, reopen, or invalidate tasks, update their content and placement, and add or remove tags.
  • Checklist restructuring and cleanup: Have your agent replace an existing task hierarchy with an updated outline or permanently remove tasks and their subtasks.

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 Checkvist 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 Checkvist 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: ['checkvist']
    }
);

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "checkvist-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 Checkvist MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Checkvist 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 Checkvist 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 Checkvist 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: ['checkvist']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "checkvist-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 Checkvist 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 Checkvist 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

Supported Tools

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

Create Checklist

Create a Checkvist checklist with a name and optional visibility and tags.

Create Task

Create a task in a checklist, optionally under a parent and with tags, smart-syntax due date, position, priority, or assignees.

Delete Checklist

Mark a Checkvist checklist for deletion.

Delete Task

Permanently delete a Checkvist task and all of its child tasks.

Get Checklist

Get one Checkvist checklist's metadata by ID.

Get Tasks Outline

Read a checklist as a recommended markdown task outline, with controls for closed tasks, depth, compact output, or top-level preview.

Get Tasks Page

Read one explicit 1-based page of a large Checkvist checklist.

Get Task Tree

Read one Checkvist task and its subtasks as a markdown outline, optionally including closed items or notes and limiting depth.

Import Tasks

Create multiple hierarchical tasks from Checkvist indented text.

List Checklists

List checklists available to the authenticated Checkvist user, optionally including archived lists or omitting statistics for a faster response.

Replace Tasks

Delete every existing task in a checklist, then replace the hierarchy from Checkvist indented text or OPML.

Search Tasks

Search tasks across the user's Checkvist checklists using words and operators for due date (^ or due:), tag (# or tag:), state (in:), and notes (has:note).

Set Task Status

Set a task's status to closed, reopened, or invalidated using Checkvist's dedicated status operations.

Update Checklist

Update an existing checklist's name, visibility, or complete tag set.

Update Task

Update task content, hierarchy, replacement tags, due date, position, priority, assignees, or smart-syntax parsing.

Update Task Tags

Add and remove task tags without replacing the complete tag set.

FRAMEWORKS

How to build Checkvist MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Checkvist MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Checkvist MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Checkvist MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Checkvist MCP with Claude Cowork

Codex logo

Codex

Use Checkvist MCP with Codex

OpenClaw logo

OpenClaw

Use Checkvist MCP with OpenClaw

Hermes logo

Hermes

Use Checkvist MCP with Hermes

CLI logo

CLI

Use Checkvist MCP with CLI

Google ADK logo

Google ADK

Use Checkvist MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Checkvist MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Checkvist MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Checkvist MCP with LlamaIndex

CrewAI logo

CrewAI

Use Checkvist MCP with CrewAI

FAQ

Frequently asked questions

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

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

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