How to integrate Shopify MCP with LangChain

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Introduction

This guide walks you through connecting Shopify to LangChain using the Composio tool router. By the end, you'll have a working Shopify agent that can create a new product called 'summer t-shirt', add product id 1234 to 'holiday specials' collection, delete the image with id 5678 from product id 4321, create a new customer with email john@example.com through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Shopify project to Composio
  • Create a Tool Router MCP session for Shopify
  • Initialize an MCP client and retrieve Shopify tools
  • Build a LangChain agent that can interact with Shopify
  • 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 Shopify MCP server, and what's possible with it?

The Shopify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Shopify account. It provides structured and secure access to your store, so your agent can perform actions like managing products, processing orders, handling collections, organizing images, and managing customers on your behalf.

  • Product management and automation: Let your agent create new products, update existing listings, or delete products from your Shopify store quickly and accurately.
  • Order creation and fulfillment: Direct your agent to generate new orders, associate them with customers, and streamline your sales process with minimal manual input.
  • Collection organization: Ask your agent to create custom collections, add products to collections, or remove collections to keep your store categories organized and up to date.
  • Product image handling: Have your agent add new images to products, count existing images for inventory tracking, or remove outdated images from your catalog.
  • Customer management: Automate the creation of new customer records, making it easy to onboard shoppers and keep your CRM current without lifting a finger.

Supported Tools & Triggers

Tools
Add product to custom collectionAdds a product to an existing *custom collection*, optionally specifying its `position` if the collection is manually sorted.
Count product imagesRetrieves the total count of images for a shopify product, useful for inventory management or display logic; the provided `product id` must exist in the store.
Create a custom collectionCreates a new custom collection in a shopify store, requiring a unique title for manually curated product groupings (e.
Create CustomerTool to create a new customer in shopify.
Create an orderCreates a new order in shopify, typically requiring line items; if `customer id` is provided, it must correspond to an existing customer.
Create a productCreates a new product in a shopify store; a product title is generally required.
Create Product ImageTool to create a new product image for a given product.
Delete custom collectionPermanently deletes a custom collection from a shopify store using its `collection id`; this action is irreversible and requires a valid, existing `collection id`.
Delete a productDeletes a specific, existing product from a shopify store using its unique product id; this action is irreversible.
Delete product imageDeletes a specific image from a product in shopify, requiring the `product id` of an existing product and the `image id` of an image currently associated with that product.
Get All CustomersRetrieves customer records from a shopify store, with options for filtering, selecting specific fields, and paginating through the results.
Get collection by IDRetrieves a specific shopify collection by its `collection id`, optionally filtering returned data to specified `fields`.
Get collectsRetrieves a list of collects from a shopify store, where a collect links a product to a custom collection.
Get collects countRetrieves the total count of collects (product-to-collection associations) in a shopify store.
Get custom collectionsRetrieves a list of custom collections from a shopify store, optionally filtered by ids, product id, or handle.
Get custom collections countRetrieves the total number of custom collections in a shopify store.
Get CustomerRetrieves detailed information for a specific customer from a shopify store, provided their valid and existing `customer id`.
Get customer ordersRetrieves all orders for a specific, existing customer in shopify using their unique customer id.
Get order listRetrieves a list of orders from shopify using default api settings and filters.
Get order by idRetrieves a specific shopify order by its unique id, which must correspond to an existing order.
Get productRetrieves details for an existing shopify product using its unique product id.
Get product imageRetrieves detailed information for a specific product image, identified by its id and its associated product id, from a shopify store.
Get Product ImagesRetrieves all images for a shopify product, specified by its `product id` which must correspond to an existing product.
Get productsRetrieves a list of products from a shopify store.
Get products countRetrieves the total, unfiltered count of all products in a shopify store.
Get products in collectionRetrieves all products within a specified shopify collection, requiring a valid `collection id`.
Get Shop DetailsRetrieves comprehensive administrative information about the authenticated shopify store, as defined by the shopify api.
Update OrderUpdates the phone number for an existing shopify order, identified by its id; pass `phone=none` to remove the current phone number.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

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

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Shopify functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
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 Shopify tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Shopify
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['shopify']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "shopify-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Shopify MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Shopify tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Shopify related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Shopify and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['shopify']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "shopify-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Shopify related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Shopify 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.

How to build Shopify MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Shopify MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Shopify MCP with Claude Agent SDK

Google ADK

Google ADK

Use Shopify MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Shopify MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Shopify MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Shopify MCP with LlamaIndex

CrewAI

CrewAI

Use Shopify MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Shopify MCP?

With a standalone Shopify MCP server, the agents and LLMs can only access a fixed set of Shopify tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Shopify and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

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 Shopify tools.

Can I manage the permissions and scopes for Shopify while using Tool Router?

Yes, absolutely. You can configure which Shopify 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.

How safe is my data with Composio Tool Router?

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 Shopify data and credentials are handled as safely as possible.

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