# How to integrate Re amaze MCP with Autogen ```json { "title": "How to integrate Re amaze MCP with Autogen", "toolkit": "Re amaze", "toolkit_slug": "re_amaze", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/re_amaze/framework/autogen", "markdown_url": "https://composio.dev/toolkits/re_amaze/framework/autogen.md", "updated_at": "2026-05-12T10:23:25.810Z" } ``` ## Introduction This guide walks you through connecting Re amaze to AutoGen using the Composio tool router. By the end, you'll have a working Re amaze agent that can list all tags used in recent reports, show most popular response templates for support, analyze tag trends from last week's conversations through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Re amaze account through Composio's Re amaze MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Re amaze with - [OpenAI Agents SDK](https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/re_amaze/framework/codex) - [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw) - [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/re_amaze/framework/cli) - [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk) - [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/re_amaze/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 Re amaze - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Re amaze tools - Run a live chat loop where you ask the agent to perform Re amaze 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 Re amaze MCP server, and what's possible with it? The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Re:amaze account. It provides structured and secure access to your customer support environment, so your agent can retrieve report tags, access canned response templates, and streamline support workflows for your team. - Tag usage analytics: Instantly pull all tags used in reports so your agent can analyze conversation trends, monitor support topics, or generate insights about ticket categorization. - Effortless response template retrieval: Fetch your brand's pre-built response templates, making it easy for the agent to suggest or automate consistent replies across channels. - Streamlined reply suggestions: Quickly surface relevant canned responses for agents or bots to use, speeding up customer replies and ensuring brand consistency. - Support workflow automation: Leverage tags and templates to help your agent automate repetitive customer support tasks and standardize communication processes. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RE_AMAZE_GET_REPORTS_TAGS` | Get report tags | Tool to retrieve a list of tags used in reports. Use when analyzing tag usage metrics across conversations. | | `RE_AMAZE_GET_RESPONSE_TEMPLATES` | Get Response Templates | Tool to retrieve response templates for the brand. Use when you need canned responses to streamline replies. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Re amaze. Instead of manually wiring Re amaze 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 Re amaze 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 Re amaze 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 Re amaze 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 Re amaze 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 Re amaze session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["re_amaze"] ) 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 Re amaze 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 Re amaze assistant agent with MCP tools agent = AssistantAgent( name="re_amaze_assistant", description="An AI assistant that helps with Re amaze 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 Re amaze 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 Re amaze 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 Re amaze session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["re_amaze"] ) 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 Re amaze assistant agent with MCP tools agent = AssistantAgent( name="re_amaze_assistant", description="An AI assistant that helps with Re amaze 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 Re amaze 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 Re amaze 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 Re amaze, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Re amaze MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/re_amaze/framework/codex) - [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw) - [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/re_amaze/framework/cli) - [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk) - [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/re_amaze/framework/crew-ai) ## Related Toolkits - [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. 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Use it to quickly find and verify professional email addresses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Re amaze MCP? With a standalone Re amaze MCP server, the agents and LLMs can only access a fixed set of Re amaze tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Re amaze 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 Re amaze tools. ### Can I manage the permissions and scopes for Re amaze while using Tool Router? Yes, absolutely. You can configure which Re amaze 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 Re amaze data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)