How to integrate Google Tasks MCP with LangChain

This guide walks you through connecting Google Tasks to LangChain using the Composio tool router. By the end, you'll have a working Google Tasks agent that can add a new task to your work list, list all tasks due this week, delete completed tasks from your shopping list through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Google Tasks account through Composio's Google Tasks MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Google Tasks logoGoogle Tasks
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Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem.

16 Tools5 Triggers

Introduction

This guide walks you through connecting Google Tasks to LangChain using the Composio tool router. By the end, you'll have a working Google Tasks agent that can add a new task to your work list, list all tasks due this week, delete completed tasks from your shopping list through natural language commands.

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

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

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

The Google Tasks MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google Tasks account. It provides structured and secure access to your to-do lists and tasks, so your agent can create task lists, add or update tasks, reorganize and clean up your lists, and fetch or manage your action items automatically.

  • Intelligent task list management: Ask your agent to create new to-do lists, fetch existing ones, or remove lists you no longer need—all without manual clicks.
  • Automated task creation and updates: Let your agent add new tasks, set due dates, or update existing to-dos to keep your lists current and organized.
  • Efficient task organization and movement: Move tasks between lists, reorder them, or set parent/child relationships so your priorities always stay clear.
  • Fast cleanup and deletion: Direct your agent to clear completed tasks or delete specific items and lists, helping you declutter swiftly and securely.
  • Detailed task retrieval and review: Have your agent pull details on any task or list so you can review upcoming deadlines, notes, and status at a glance.

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 Google Tasks 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 Google Tasks 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: ['googletasks']
    }
);

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

Configure the agent with the MCP URL

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

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

Batch Execute Google Tasks Operations

Executes multiple Google Tasks API operations in a single HTTP batch request and returns structured per-item results.

Clear tasks

Permanently and irreversibly clears all completed tasks from a specified Google Tasks list; this action is destructive, idempotent, and cannot be undone.

Create a task list

Creates a new task list with the specified title and returns a tasklist_id.

Delete task

Deletes a specified task from a Google Tasks list.

Delete task list

Permanently deletes an existing Google Task list, identified by `tasklist_id`, along with all its tasks; this operation is irreversible.

Get Task

Retrieve a specific Google Task.

Get task list

Retrieves a specific task list from the user's Google Tasks if the `tasklist_id` exists for the authenticated user.

Insert Task

Creates a new task in a given `tasklist_id`, optionally as a subtask of an existing `task_parent` or positioned after an existing `task_previous` sibling, where both `task_parent` and `task_previous` must belong to the same `tasklist_id` if specified.

List All Tasks Across All Lists

Tool to list all tasks across all of the user's task lists with optional filters.

List task lists

Fetches the authenticated user's task lists from Google Tasks; results may be paginated.

List Tasks

Retrieves tasks from a Google Tasks list; all date/time strings must be RFC3339 UTC, and `showCompleted` must be true if `completedMin` or `completedMax` are specified.

Move Task

Moves the specified task to another position in the task list or to a different task list.

Patch Task

Partially updates an existing task (identified by `task_id`) within a specific Google Task list (identified by `tasklist_id`), modifying only the provided attributes from `TaskInput` (e.

Patch task list

Updates the title of an existing Google Tasks task list.

Update Task (Full Replacement)

Tool to fully replace an existing Google Task using PUT method.

Update Task List

Updates the authenticated user's specified task list.

FRAMEWORKS

How to build Google Tasks MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Google Tasks MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Google Tasks MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Google Tasks MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Google Tasks MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Google Tasks MCP with Claude Cowork

Codex logo

Codex

Use Google Tasks MCP with Codex

Cursor logo

Cursor

Use Google Tasks MCP with Cursor

OpenClaw logo

OpenClaw

Use Google Tasks MCP with OpenClaw

Hermes logo

Hermes

Use Google Tasks MCP with Hermes

CLI logo

CLI

Use Google Tasks MCP with CLI

Google ADK logo

Google ADK

Use Google Tasks MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Google Tasks MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Google Tasks MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Google Tasks MCP with LlamaIndex

CrewAI logo

CrewAI

Use Google Tasks MCP with CrewAI

OpenCode logo

OpenCode

Use Google Tasks MCP with OpenCode

VS Code logo

VS Code

Use Google Tasks MCP with VS Code

FAQ

Frequently asked questions

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

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

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