# How to integrate Ragic MCP with Autogen ```json { "title": "How to integrate Ragic MCP with Autogen", "toolkit": "Ragic", "toolkit_slug": "ragic", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/ragic/framework/autogen", "markdown_url": "https://composio.dev/toolkits/ragic/framework/autogen.md", "updated_at": "2026-05-12T10:23:18.149Z" } ``` ## Introduction This guide walks you through connecting Ragic to AutoGen using the Composio tool router. By the end, you'll have a working Ragic agent that can find all customer records updated today, search invoices with status pending payment, list project tasks assigned to john doe through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Ragic account through Composio's Ragic MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Ragic with - [OpenAI Agents SDK](https://composio.dev/toolkits/ragic/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ragic/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ragic/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ragic/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ragic/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ragic/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ragic/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ragic/framework/cli) - [Google ADK](https://composio.dev/toolkits/ragic/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ragic/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/ragic/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/ragic/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/ragic/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/ragic/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 Ragic - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Ragic tools - Run a live chat loop where you ask the agent to perform Ragic 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 Ragic MCP server, and what's possible with it? The Ragic MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ragic account. It provides structured and secure access to your Ragic databases, enabling your agent to search, filter, and analyze records just like you would on the Ragic platform. - Powerful record search: Instruct your agent to quickly find specific database records using keywords, fields, or filters across your Ragic tables. - Automated data analysis: Let your agent scan through records to extract insights, summarize trends, or identify outliers without manual effort. - Custom query execution: Have the agent run tailored searches to surface exactly the information you need from your custom Ragic forms and sheets. - Real-time database lookup: Ask your agent to instantly retrieve up-to-date details from any Ragic database for reporting, decision-making, or workflow automation. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RAGIC_DELETE_ENTRY` | Delete Entry | Tool to delete an entry from a Ragic sheet. Use when you need to remove a specific record from a sheet by its record ID. | | `RAGIC_DOWNLOAD_FILE` | Download File | Tool to download uploaded files, images, or email attachments from Ragic. Use the file reference returned from API responses (e.g., from record fields). | | `RAGIC_GET_ACTION_BUTTONS` | Get Action Buttons | Tool to retrieve the list of available action buttons on a Ragic sheet. Returns action button IDs and names. Use category=massOperation to get buttons available for mass operations. | | `RAGIC_GET_CUSTOM_PRINT_REPORT` | Get Custom Print Report | Tool to retrieve a custom print report for a Ragic record in various formats (pdf, png, docx). Use when you need to generate and download a formatted report using a specific template. | | `RAGIC_GET_ENTRY` | Get Entry | Tool to retrieve a single entry from a Ragic sheet by its record ID. Use when you need to fetch specific record data including field values and optional subtable data. | | `RAGIC_GET_RECORD_AS_PDF` | Get Record as PDF | Tool to retrieve a PDF version of a Ragic record. Use when you need to export or download a record in PDF format. The PDF will contain the full record data in a formatted document. | | `RAGIC_SEARCH_RECORDS` | Search Records | Search Records | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Ragic MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Ragic. Instead of manually wiring Ragic 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 Ragic 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 Ragic 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 Ragic 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 Ragic 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 Ragic session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["ragic"] ) 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 Ragic 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 Ragic assistant agent with MCP tools agent = AssistantAgent( name="ragic_assistant", description="An AI assistant that helps with Ragic 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 Ragic 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 Ragic 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 Ragic session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["ragic"] ) 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 Ragic assistant agent with MCP tools agent = AssistantAgent( name="ragic_assistant", description="An AI assistant that helps with Ragic 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 Ragic 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 Ragic 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 Ragic, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Ragic MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/ragic/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ragic/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ragic/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ragic/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ragic/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ragic/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ragic/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ragic/framework/cli) - [Google ADK](https://composio.dev/toolkits/ragic/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ragic/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/ragic/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/ragic/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/ragic/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/ragic/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Ragic MCP? With a standalone Ragic MCP server, the agents and LLMs can only access a fixed set of Ragic tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Ragic 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 Ragic tools. ### Can I manage the permissions and scopes for Ragic while using Tool Router? Yes, absolutely. You can configure which Ragic 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 Ragic data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)