How to integrate Printify MCP with LangChain

This guide walks you through connecting Printify to LangChain using the Composio tool router. By the end, you'll have a working Printify agent that can create draft products, review your orders, calculate shipping, list print providers, and manage webhooks through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Printify account through Composio's Printify MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Printify is a print-on-demand platform for creating and selling custom products through connected merchant shops. It helps teams manage product catalogs, fulfillment, orders, uploads, and webhooks from one place.

25 Tools

Introduction

This guide walks you through connecting Printify to LangChain using the Composio tool router. By the end, you'll have a working Printify agent that can create draft products, review your orders, calculate shipping, list print providers, and manage webhooks through natural language commands.

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

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

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

The Printify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Printify account. It provides structured and secure access to your shops, catalog, products, orders, images, and webhooks, so your agent can create products, manage listings, review orders, estimate shipping, and monitor shop events on your behalf.

  • Product creation and publishing: Have your agent create product drafts, update product details, publish selected changes to a connected sales channel, mark products as unpublished, or delete products.
  • Catalog and fulfillment research: Let the agent search product blueprints, review blueprint details, compare print providers and variants, and check available shipping methods and costs.
  • Order and shipping review: Direct your agent to list orders, retrieve complete order details, filter orders by fulfillment status or SKU, and calculate shipping estimates for proposed purchases.
  • Shop and image management: Instruct your agent to list your Printify shops, review products, upload artwork to your media library, list uploaded images, or archive images you no longer need.
  • Webhook management and testing: Have your agent create, review, update, delete, and test webhook subscriptions that notify your systems about Printify events.

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 Printify 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 Printify 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: ['printify']
    }
);

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

Configure the agent with the MCP URL

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

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

Archive Uploaded Image

Irreversibly archive an image in the connected merchant's Printify media library.

Calculate Order Shipping

Calculate available shipping-cost estimates for proposed line items and a destination.

Create Draft Product

Create a new product draft in a Printify shop.

Create Webhook

Create a persistent webhook subscription for a Printify shop.

Delete Product

Permanently delete a product from a Printify shop.

Delete Webhook

Permanently delete a Printify webhook subscription so future events are no longer delivered.

Get Blueprint

Get the full catalog record for one Printify product blueprint, including its description and images.

Get Order

Get complete details for one Printify order, including its recipient address, line items, status, shipping method, shipments, and timestamps.

Get Product

Get the complete Printify record for one product in a shop, including variants, print areas, images, visibility, and sales-channel properties.

Get Catalog Shipping Details

Get per-variant shipping costs and handling-time ranges for one advertised V2 shipping method.

List Blueprint Print Providers

List print providers that can fulfill a specific Printify blueprint.

List Blueprint Variants

List purchasable variants and print placeholders for a blueprint and print provider.

List Orders

Return one page of orders in a Printify shop, optionally filtered by provider status or exact SKU.

List Products

Return one page of products in a Printify shop.

List Catalog Shipping Methods

List the V2 shipping methods actually available for a Printify blueprint and print provider.

List Shops

List shops available to the connected Printify merchant account.

List Uploaded Images

Return one page of images in the connected merchant's Printify media library.

List Webhooks

List webhook subscriptions configured for a Printify shop.

Publish Product Changes

Start asynchronous publication of one or more fields selected in fields_to_publish from Printify to the shop's connected sales channel.

Search Catalog Blueprints

Search Printify's product blueprint catalog by case-insensitive text in title, brand, or model.

Simulate Webhook Event

Ask Printify to send a simulated event to an existing webhook's external callback URL.

Mark Product Unpublished

Notify Printify that a product has been unpublished from the connected sales channel.

Update Product Draft

Update selected fields of an existing Printify product.

Update Webhook URL

Replace the callback URL of an existing Printify webhook subscription.

Upload Image

Add an image to the connected merchant's Printify media library from either a public HTTP(S) URL or base64 content.

FRAMEWORKS

How to build Printify MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Printify MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Printify MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Printify MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Printify MCP with Claude Cowork

Codex logo

Codex

Use Printify MCP with Codex

OpenClaw logo

OpenClaw

Use Printify MCP with OpenClaw

Hermes logo

Hermes

Use Printify MCP with Hermes

CLI logo

CLI

Use Printify MCP with CLI

Google ADK logo

Google ADK

Use Printify MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Printify MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Printify MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Printify MCP with LlamaIndex

CrewAI logo

CrewAI

Use Printify MCP with CrewAI

FAQ

Frequently asked questions

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

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

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