How to integrate Printify MCP with Autogen

This guide walks you through connecting Printify to AutoGen 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 AutoGen 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 AutoGen 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 AutoGen 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 ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Printify
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Printify tools
  • Run a live chat loop where you ask the agent to perform Printify operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Printify account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Printify via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Printify connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Printify session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["printify"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Printify tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Printify assistant agent with MCP tools
    agent = AssistantAgent(
        name="printify_assistant",
        description="An AI assistant that helps with Printify operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Printify tools from the workbench
8

Run the interactive chat loop

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

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Printify tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Printify and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Printify session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["printify"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Printify assistant agent with MCP tools
        agent = AssistantAgent(
            name="printify_assistant",
            description="An AI assistant that helps with Printify operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Printify through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Printify, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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

LangChain logo

LangChain

Use Printify MCP with LangChain

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. Autogen 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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