How to integrate Kanbanize MCP with LangChain

This guide walks you through connecting Kanbanize to LangChain using the Composio tool router. By the end, you'll have a working Kanbanize agent that can move all 'in progress' cards to 'done', create a kanban card for new feature request, list all overdue tasks in your boards through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Kanbanize account through Composio's Kanbanize MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for freeGet a demo
Kanbanize logoKanbanize
Api Key

Kanbanize is a project management tool for Lean and Kanban workflows. Streamline product and engineering team collaboration with visual boards and analytics.

24 Tools

Introduction

This guide walks you through connecting Kanbanize to LangChain using the Composio tool router. By the end, you'll have a working Kanbanize agent that can move all 'in progress' cards to 'done', create a kanban card for new feature request, list all overdue tasks in your boards through natural language commands.

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

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

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

The Kanbanize MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kanbanize account. It provides structured and secure access so your agent can perform Kanbanize operations on your behalf.

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 Kanbanize 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 Kanbanize 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: ['kanbanize']
    }
);

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

Configure the agent with the MCP URL

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

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

Add a comment to a card

Tool to add a comment to a Kanbanize card.

Check Board Milestone

Tool to check if a milestone is available on the specified board.

Check User Is Card Watcher

Tool to check if a user is a watcher of a specific card.

Delete Board

Tool to delete a board by its ID.

Delete Card

Tool to delete a card from the Kanbanize board.

Delete Tag

Tool to delete a tag from Kanbanize.

Delete Workflow

Tool to delete a workflow for the specified board.

Get Board Block Reasons

Tool to get a list of block reasons available on a board.

Get Board Card Templates

Tool to retrieve a list of card templates available on a Kanbanize board.

Get Child Cards

Tool to retrieve a list of child cards for a specified parent card.

Get Column

Tool to get the details of a specific column from a Kanbanize board.

Get Columns

Tool to get a list of columns for a specific board in Kanbanize.

Get Custom Fields

Tool to retrieve a list of custom fields from Kanbanize with optional filtering.

Get Stickers

Tool to retrieve a list of stickers with optional filtering by sticker IDs, label, availability, and enabled status.

Get User

Tool to get the details of a specified user in Kanbanize.

Get workflow cycle time columns

Tool to retrieve workflow's cycle time columns from a Kanbanize board.

Get Workspace Data Fields

Tool to retrieve a list of data fields available on a workspace.

Remove Board Block Reason

Tool to make a block reason unavailable on a board.

Remove Child Card

Tool to remove the link between a parent card and a child card.

Set card block reason

Tool to block a Kanbanize card by setting a block reason.

Update Board Sticker

Tool to update the properties of a sticker for the specified board.

Update Data Field Workspaces

Tool to add, update, or remove a data field on one or more workspaces via batch operations.

Update Lane Default Setting

Tool to update the default value of a specific lane setting in Kanbanize.

Update Tag

Tool to update the specified tag in Kanbanize.

FRAMEWORKS

How to build Kanbanize MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Kanbanize MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Kanbanize MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Kanbanize MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Kanbanize MCP with Claude Cowork

Codex logo

Codex

Use Kanbanize MCP with Codex

OpenClaw logo

OpenClaw

Use Kanbanize MCP with OpenClaw

Hermes logo

Hermes

Use Kanbanize MCP with Hermes

CLI logo

CLI

Use Kanbanize MCP with CLI

Google ADK logo

Google ADK

Use Kanbanize MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Kanbanize MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Kanbanize MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Kanbanize MCP with LlamaIndex

CrewAI logo

CrewAI

Use Kanbanize MCP with CrewAI

FAQ

Frequently asked questions

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

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

Start with Kanbanize.It takes 30 seconds.

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

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

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.

Tomisin Jenrola
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
Jerome Leclanche
Co-Founder, Ingram Technologies