# How to integrate Plasmic MCP with Autogen ```json { "title": "How to integrate Plasmic MCP with Autogen", "toolkit": "Plasmic", "toolkit_slug": "plasmic", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/plasmic/framework/autogen", "markdown_url": "https://composio.dev/toolkits/plasmic/framework/autogen.md", "updated_at": "2026-05-12T10:22:15.970Z" } ``` ## Introduction This guide walks you through connecting Plasmic to AutoGen using the Composio tool router. By the end, you'll have a working Plasmic agent that can generate global context config for react app, update authentication provider in plasmic project, add analytics context to plasmic loader setup through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Plasmic account through Composio's Plasmic MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Plasmic with - [OpenAI Agents SDK](https://composio.dev/toolkits/plasmic/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/plasmic/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/plasmic/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/plasmic/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/plasmic/framework/codex) - [OpenClaw](https://composio.dev/toolkits/plasmic/framework/openclaw) - [Hermes](https://composio.dev/toolkits/plasmic/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/plasmic/framework/cli) - [Google ADK](https://composio.dev/toolkits/plasmic/framework/google-adk) - [LangChain](https://composio.dev/toolkits/plasmic/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/plasmic/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/plasmic/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/plasmic/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/plasmic/framework/crew-ai) ## 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 Plasmic - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Plasmic tools - Run a live chat loop where you ask the agent to perform Plasmic 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 Plasmic MCP server, and what's possible with it? The Plasmic MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Plasmic account. It provides structured and secure access to your Plasmic environment, so your agent can generate global configuration snippets, automate design system setup, and streamline your web project workflows on your behalf. - Generate global context configuration: Instantly create the configuration snippets needed for your Plasmic loader, saving time and reducing manual errors. - Automate design system integration: Let your agent set up and update global contexts for your React providers, ensuring consistent theming and behavior across your app. - Simplify onboarding for new projects: Quickly bootstrap new Plasmic projects with pre-configured global settings, making it easy for teams to get started. - Maintain and update component contexts: Effortlessly refresh or change global context settings as your design system evolves, keeping your web components in sync. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PLASMIC_GLOBAL_ACTIONS_PROVIDER` | Plasmic Global Actions Provider | Tool to generate the globalContexts configuration snippet for Plasmic loader. Use when you need to configure global contexts after defining your React providers. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Plasmic MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Plasmic. Instead of manually wiring Plasmic APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Plasmic account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Plasmic via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables 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 Plasmic connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Plasmic tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```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 Plasmic session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["plasmic"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen 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 ```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")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Plasmic tools from the workbench ```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 Plasmic assistant agent with MCP tools agent = AssistantAgent( name="plasmic_assistant", description="An AI assistant that helps with Plasmic operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Plasmic 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 ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Plasmic 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") ``` ## Complete Code ```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 Plasmic session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["plasmic"] ) 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 Plasmic assistant agent with MCP tools agent = AssistantAgent( name="plasmic_assistant", description="An AI assistant that helps with Plasmic 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 Plasmic 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 Plasmic 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 Plasmic, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Plasmic MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/plasmic/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/plasmic/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/plasmic/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/plasmic/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/plasmic/framework/codex) - [OpenClaw](https://composio.dev/toolkits/plasmic/framework/openclaw) - [Hermes](https://composio.dev/toolkits/plasmic/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/plasmic/framework/cli) - [Google ADK](https://composio.dev/toolkits/plasmic/framework/google-adk) - [LangChain](https://composio.dev/toolkits/plasmic/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/plasmic/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/plasmic/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/plasmic/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/plasmic/framework/crew-ai) ## Related Toolkits - [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing. - [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams. - [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content. - [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates. - [Canva](https://composio.dev/toolkits/canva) - Canva is a drag-and-drop design suite for creating professional graphics, presentations, and marketing materials. It makes it easy for anyone to design with beautiful templates and a vast library of elements. - [Claid ai](https://composio.dev/toolkits/claid_ai) - Claid.ai delivers AI-driven image editing APIs for tasks like background removal, upscaling, and color correction. It helps automate and enhance image workflows with powerful, developer-friendly tools. - [Cloudinary](https://composio.dev/toolkits/cloudinary) - Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally. - [Cults](https://composio.dev/toolkits/cults) - Cults is a digital marketplace for 3D printing models, connecting designers and makers. It lets creators share, sell, and discover a huge variety of printable designs easily. - [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks. - [Dreamstudio](https://composio.dev/toolkits/dreamstudio) - DreamStudio is Stability AI’s platform for generating and editing images with AI. It lets you easily turn ideas into stunning visuals, fast. - [Dynapictures](https://composio.dev/toolkits/dynapictures) - Dynapictures is a cloud-based platform for generating personalized images at scale. Instantly create hundreds of custom visuals using your data sources, like Google Sheets. - [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools. - [Gamma](https://composio.dev/toolkits/gamma) - Gamma is an AI-powered platform for making beautiful, interactive presentations and documents. It lets anyone create and share engaging decks with minimal effort. - [Html to image](https://composio.dev/toolkits/html_to_image) - Html to image converts HTML and CSS into images or captures web page screenshots. Instantly generate visuals from code or web content—no manual screenshots needed. - [Imagior](https://composio.dev/toolkits/imagior) - Imagior is an AI-powered image generation platform that lets you create and customize images using dynamic templates and APIs. Perfect for businesses and creators needing fast, scalable visuals without design hassle. - [Imejis io](https://composio.dev/toolkits/imejis_io) - Imejis io is an API-based image generation platform with powerful customization and template support. It lets you create and modify images in seconds, no manual design work required. - [Imgix](https://composio.dev/toolkits/imgix) - Imgix is a real-time image processing and delivery service for developers. It helps you optimize, transform, and deliver images efficiently at any scale. - [Kraken io](https://composio.dev/toolkits/kraken_io) - Kraken.io is an image optimization and compression platform. It helps you shrink image file sizes while keeping visual quality intact. - [Logo dev](https://composio.dev/toolkits/logo_dev) - Logo.dev is an API and database for high-resolution company logos and brand metadata. Instantly fetch official logos from any domain without scraping or manual searching. - [Miro](https://composio.dev/toolkits/miro) - Miro is a collaborative online whiteboard platform for teams to brainstorm, design, and manage projects visually. It streamlines teamwork by enabling real-time idea sharing, diagramming, and workflow planning in a single space. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Plasmic MCP? With a standalone Plasmic MCP server, the agents and LLMs can only access a fixed set of Plasmic tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Plasmic and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Autogen? 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 Plasmic tools. ### Can I manage the permissions and scopes for Plasmic while using Tool Router? Yes, absolutely. You can configure which Plasmic 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 Plasmic data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)