How to integrate Lemon squeezy MCP with LangChain

This guide walks you through connecting Lemon squeezy to LangChain using the Composio tool router. By the end, you'll have a working Lemon squeezy agent that can list all recent orders for your store, create a new customer with email address, show all active discounts available now through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Lemon squeezy account through Composio's Lemon squeezy MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Lemon Squeezy is a payments and subscription platform built for software companies. It makes managing payments, taxes, and customer subscriptions effortless.

32 Tools

Introduction

This guide walks you through connecting Lemon squeezy to LangChain using the Composio tool router. By the end, you'll have a working Lemon squeezy agent that can list all recent orders for your store, create a new customer with email address, show all active discounts available now through natural language commands.

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

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

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

The Lemon Squeezy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lemon Squeezy account. It provides structured and secure access to your e-commerce operations, so your agent can perform actions like managing customers, tracking orders, retrieving discounts, and handling subscriptions on your behalf.

  • Customer creation and management: Quickly add new customers or pull up detailed customer lists, streamlining your onboarding and support flows.
  • Order tracking and retrieval: Effortlessly fetch a paginated list of orders or drill down to specific order items to monitor sales activity and fulfillment status.
  • Discount and affiliate insights: Retrieve all active discounts or affiliate partners, making it a breeze to analyze promotions and partner performance.
  • Checkout and price listing: Access and filter all checkouts or prices across your stores and variants, helping you keep tabs on your sales funnels and product offerings.
  • License key and redemption management: List all license key instances and track discount redemptions, ensuring easy oversight of software access and promotional usage.

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 Lemon squeezy 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 Lemon squeezy 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: ['lemon_squeezy']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Customer

Tool to create a new customer.

Create Discount

Tool to create a new discount code in Lemon Squeezy.

Create Webhook

Tool to create a new webhook for receiving event notifications.

Delete Discount

Tool to delete a discount by its ID.

Delete Webhook

Tool to delete a webhook.

List All Affiliates

Tool to list all affiliates.

List All Checkouts

Tool to list all checkouts.

List All Customers

Retrieves a paginated list of all customers from your Lemon Squeezy store(s).

List All Discount Redemptions

Tool to list all discount redemptions.

List All Discounts

Tool to list all discounts.

List All Files

Retrieves a paginated list of files from Lemon Squeezy.

List All License Key Instances

Tool to list all license key instances.

List All License Keys

Tool to list all license keys.

List All Order Items

Tool to list all order items.

List All Orders

Tool to list all orders.

List All Prices

Tool to list all prices.

List All Products

List all products from your Lemon Squeezy store with pagination and filtering.

List All Stores

Retrieves a paginated list of all stores belonging to the authenticated Lemon Squeezy account.

List All Subscription Invoices

Tool to list all subscription invoices.

List All Subscription Items

Tool to list all subscription items.

List All Subscriptions

Tool to list all subscriptions.

List All Usage Records

Retrieves all usage records from Lemon Squeezy, with optional filtering and pagination.

List All Variants

Retrieves a paginated list of product variants from Lemon Squeezy.

List All Webhooks

Tool to list all webhooks.

Retrieve Authenticated User

Tool to retrieve the currently authenticated user from Lemon Squeezy.

Retrieve Customer

Tool to retrieve a specific customer by their ID.

Retrieve Discount

Tool to retrieve a single discount by ID.

Retrieve Store

Tool to retrieve a store by its ID.

Retrieve Webhook

Tool to retrieve a webhook by its ID.

Update Customer

Tool to update an existing customer with the given ID.

Update Webhook

Tool to update an existing webhook.

Validate License

Tool to validate a license key and optionally a specific license key instance.

FRAMEWORKS

How to build Lemon squeezy MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Lemon squeezy MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Lemon squeezy MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Lemon squeezy MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Lemon squeezy MCP with Claude Cowork

Codex logo

Codex

Use Lemon squeezy MCP with Codex

OpenClaw logo

OpenClaw

Use Lemon squeezy MCP with OpenClaw

Hermes logo

Hermes

Use Lemon squeezy MCP with Hermes

CLI logo

CLI

Use Lemon squeezy MCP with CLI

Google ADK logo

Google ADK

Use Lemon squeezy MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Lemon squeezy MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Lemon squeezy MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Lemon squeezy MCP with LlamaIndex

CrewAI logo

CrewAI

Use Lemon squeezy MCP with CrewAI

FAQ

Frequently asked questions

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

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

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