How to integrate Thanks io MCP with LangChain

This guide walks you through connecting Thanks io to LangChain using the Composio tool router. By the end, you'll have a working Thanks io agent that can add new customer to holiday mailing list, show all available handwritten font styles, create a mailing list for event attendees through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Thanks io account through Composio's Thanks io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Thanks io is a direct mail automation platform for sending personalized postcards, letters, and notecards. Reach your customers offline with scalable, handwritten-style mailings.

29 Tools

Introduction

This guide walks you through connecting Thanks io to LangChain using the Composio tool router. By the end, you'll have a working Thanks io agent that can add new customer to holiday mailing list, show all available handwritten font styles, create a mailing list for event attendees through natural language commands.

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

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

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

The Thanks io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Thanks io account. It provides structured and secure access to your direct mail platform, so your agent can perform actions like managing mailing lists, sending personalized postcards, choosing templates, and handling recipients automatically on your behalf.

  • Mailing list management: Effortlessly create, list, or delete mailing lists, and keep your recipient groups organized for targeted campaigns.
  • Recipient automation: Quickly add or remove recipients from mailing lists, ensuring your contacts are always up to date and ready for new mailings.
  • Personalized mail creation: Enable your agent to select from available handwriting styles or image templates, so every postcard, letter, or notecard feels truly unique.
  • Template selection and preview: Browse and choose from message and image templates to customize your direct mail content for any occasion.
  • Automated sending workflows: Trigger stored send actions to deliver mailings at the right moment, keeping your outreach timely and efficient.

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 Thanks io 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 Thanks io 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: ['thanks_io']
    }
);

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

Configure the agent with the MCP URL

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

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

Add Recipient to Mailing List

Tool to add a new recipient to a mailing list.

Create Mailing List

Tool to create a new mailing list.

Delete Mailing List

Tool to delete a mailing list.

Delete Recipient from Mailing List

Tool to remove a recipient from a mailing list.

Delete Sub-Account

Tool to delete a specific sub-account by ID.

Execute Stored Send

Tool to execute a previously created stored send.

List Handwriting Styles

Tool to retrieve available handwriting styles.

List Image Templates

Tool to retrieve a list of available image templates.

List Mailing Lists

Tool to list all mailing lists.

List Message Templates

Tool to list available message templates.

Buy Radius Search Mailing List

Tool to buy or append a radius search mailing list based on address and radius.

Preview letter send

Tool to preview a letter send as PDF.

Preview Notecard

Tool to preview a notecard send.

Preview Windowless Letter

Tool to preview a windowless letter send.

List Orders

Tool to list recent orders.

Search Orders by Recipient Street Address

Tool to search orders by recipient street address.

Create Multiple Recipients

Tool to create multiple recipients at once in a mailing list.

Delete Recipient by Address

Tool to delete a recipient by address and postal code.

Get Recipient Details

Tool to get details for a specific recipient by ID.

Search Recipients by Email

Tool to search recipients by email across mailing lists.

Update Recipient

Tool to update existing recipient details by recipient ID.

Send Postcard

Tool to send a customized postcard.

Stored Send Notecard

Tool to create a stored send for a notecard.

Stored Send Postcard

Tool to create a stored send for a postcard.

Stored Send Windowless Letter

Tool to create a stored send for a windowless letter.

Create Sub-Account

Tool to create a new sub-account.

List Sub Accounts

Tool to list all available sub-accounts.

Get Sub Account Details

Tool to retrieve details for a specific sub-account by ID.

Update Sub-Account

Tool to update details for a specific sub-account.

FRAMEWORKS

How to build Thanks io MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Thanks io MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Thanks io MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Thanks io MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Thanks io MCP with Claude Cowork

Codex logo

Codex

Use Thanks io MCP with Codex

OpenClaw logo

OpenClaw

Use Thanks io MCP with OpenClaw

Hermes logo

Hermes

Use Thanks io MCP with Hermes

CLI logo

CLI

Use Thanks io MCP with CLI

Google ADK logo

Google ADK

Use Thanks io MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Thanks io MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Thanks io MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Thanks io MCP with LlamaIndex

CrewAI logo

CrewAI

Use Thanks io MCP with CrewAI

FAQ

Frequently asked questions

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

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

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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.

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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.

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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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