# How to integrate DeepImage MCP with Autogen ```json { "title": "How to integrate DeepImage MCP with Autogen", "toolkit": "DeepImage", "toolkit_slug": "deepimage", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/deepimage/framework/autogen", "markdown_url": "https://composio.dev/toolkits/deepimage/framework/autogen.md", "updated_at": "2026-03-29T06:29:57.483Z" } ``` ## Introduction This guide walks you through connecting DeepImage to AutoGen using the Composio tool router. By the end, you'll have a working DeepImage agent that can enhance and upscale this low-res image, apply ai noise reduction to a photo, convert given image to hd quality through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a DeepImage account through Composio's DeepImage MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate DeepImage with - [OpenAI Agents SDK](https://composio.dev/toolkits/deepimage/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/deepimage/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/deepimage/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/deepimage/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/deepimage/framework/codex) - [OpenClaw](https://composio.dev/toolkits/deepimage/framework/openclaw) - [Hermes](https://composio.dev/toolkits/deepimage/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/deepimage/framework/cli) - [Google ADK](https://composio.dev/toolkits/deepimage/framework/google-adk) - [LangChain](https://composio.dev/toolkits/deepimage/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/deepimage/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/deepimage/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/deepimage/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/deepimage/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 DeepImage - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call DeepImage tools - Run a live chat loop where you ask the agent to perform DeepImage 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 DeepImage MCP server, and what's possible with it? The DeepImage MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your DeepImage account. It provides structured and secure access so your agent can perform DeepImage operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DEEPIMAGE_DELETE_JOB` | Delete Job | Tool to delete completed job result and remove images from the server. Use when cleaning up processed jobs that are no longer needed. | | `DEEPIMAGE_GET_ACCOUNT_INFO` | Get Account Information | Tool to retrieve authenticated user's profile information including credits, username, email, API key, language preference, webhook settings, and billing address. Use when you need to check account details or credit balance. | | `DEEPIMAGE_GET_JOB_RESULT` | Get Job Result | Tool to retrieve processing job result by hash. Use to check job status and get result URL when processing is complete. | | `DEEPIMAGE_PROCESS_IMAGE_ASYNC` | Process Image Async | Tool to schedule an image enhancement job and wait for the result. Performs selected enhancement options on an image and returns the result URL if processing completes within 25 seconds; otherwise returns a job hash for later retrieval. Use when you need to enhance images with options like denoising, deblurring, lighting improvements, or background removal. | | `DEEPIMAGE_PROCESS_IMAGE_SYNCHRONOUSLY` | Process Image Synchronously | Tool to process an image synchronously with Deep Image AI. Returns result URL immediately if processing completes in less than 25 seconds, otherwise returns job hash for async polling. Use for image enhancement, resizing, background removal, and other image processing tasks. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The DeepImage MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to DeepImage. Instead of manually wiring DeepImage 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 DeepImage 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 DeepImage 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 DeepImage 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 DeepImage 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 DeepImage session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["deepimage"] ) 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 DeepImage 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 DeepImage assistant agent with MCP tools agent = AssistantAgent( name="deepimage_assistant", description="An AI assistant that helps with DeepImage 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 DeepImage 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 DeepImage 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 DeepImage session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["deepimage"] ) 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 DeepImage assistant agent with MCP tools agent = AssistantAgent( name="deepimage_assistant", description="An AI assistant that helps with DeepImage 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 DeepImage 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 DeepImage 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 DeepImage, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build DeepImage MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/deepimage/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/deepimage/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/deepimage/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/deepimage/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/deepimage/framework/codex) - [OpenClaw](https://composio.dev/toolkits/deepimage/framework/openclaw) - [Hermes](https://composio.dev/toolkits/deepimage/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/deepimage/framework/cli) - [Google ADK](https://composio.dev/toolkits/deepimage/framework/google-adk) - [LangChain](https://composio.dev/toolkits/deepimage/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/deepimage/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/deepimage/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/deepimage/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/deepimage/framework/crew-ai) ## Related Toolkits - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [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. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [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. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [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. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [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. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [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. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and DeepImage MCP? With a standalone DeepImage MCP server, the agents and LLMs can only access a fixed set of DeepImage tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from DeepImage 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 DeepImage tools. ### Can I manage the permissions and scopes for DeepImage while using Tool Router? Yes, absolutely. You can configure which DeepImage 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 DeepImage data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)