How to integrate CheckFlow MCP with LangChain

This guide walks you through connecting CheckFlow to LangChain using the Composio tool router. By the end, you'll have a working CheckFlow agent that can find your checklists, inspect task details, review analytics, browse templates, and create tags through natural language commands. This guide will help you understand how to give your LangChain agent real control over a CheckFlow account through Composio's CheckFlow MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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CheckFlow is a workflow and checklist platform for repeatable processes, tasks, templates, teams, data sets, and automation. Use it to standardize operations, track process execution, and keep teams aligned.

10 Tools

Introduction

This guide walks you through connecting CheckFlow to LangChain using the Composio tool router. By the end, you'll have a working CheckFlow agent that can find your checklists, inspect task details, review analytics, browse templates, and create tags through natural language commands.

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

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

Also integrate CheckFlow 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 CheckFlow project to Composio
  • Create a Tool Router MCP session for CheckFlow
  • Initialize an MCP client and retrieve CheckFlow tools
  • Build a LangChain agent that can interact with CheckFlow
  • 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 CheckFlow MCP server, and what's possible with it?

The CheckFlow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your CheckFlow account. It provides structured and secure access to your checklists, templates, tasks, team directory, tags, analytics, and webhook subscriptions, so your agent can find checklists, inspect task details, review activity, browse team resources, and manage tags on your behalf.

  • Checklist discovery and review: Have your agent find checklists by template, name, tag, or status, then review their tasks and controls.
  • Task detail retrieval: Direct your agent to inspect a checklist task, including its controls, comments, and assignees.
  • Template and team lookup: Let the agent list active templates and their tasks, or search active team members and groups by name.
  • Analytics and subscription visibility: Instruct your agent to review checklist and task activity for a date range, or list webhook subscriptions by source and event type.
  • Tag organization: Have your agent list available team tags or create a tag without duplicating an existing tag with the same name.

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 CheckFlow 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 CheckFlow 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: ['checkflow']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Tag

Create a team tag by name, or return the existing same-name tag without creating a duplicate.

Find Checklists

Find checklist summaries for a template, optionally filtered by name, tag, or lifecycle status.

Get Analytics

Return checklist and task activity analytics for a date range.

Get Task Details

Get one checklist task with its controls, comments, and assignees.

Get Team Directory

Return active team members and groups, including each group's membership, optionally filtered by name.

List Checklist Details

Return one page of full checklist instances, including tasks and controls, with a cursor for the next page.

List Tags

List the tags available to the connected CheckFlow team.

List Templates

List active checklist templates and their keys for creating or finding checklists.

List Template Tasks

List the task keys and names defined by one checklist template.

List Webhook Subscriptions

List webhook subscriptions, optionally filtered by source and event type.

FRAMEWORKS

How to build CheckFlow MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use CheckFlow MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use CheckFlow MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use CheckFlow MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use CheckFlow MCP with Claude Cowork

Codex logo

Codex

Use CheckFlow MCP with Codex

OpenClaw logo

OpenClaw

Use CheckFlow MCP with OpenClaw

Hermes logo

Hermes

Use CheckFlow MCP with Hermes

CLI logo

CLI

Use CheckFlow MCP with CLI

Google ADK logo

Google ADK

Use CheckFlow MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use CheckFlow MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use CheckFlow MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use CheckFlow MCP with LlamaIndex

CrewAI logo

CrewAI

Use CheckFlow MCP with CrewAI

FAQ

Frequently asked questions

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

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

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