# How to integrate LLMWhisperer MCP with Autogen ```json { "title": "How to integrate LLMWhisperer MCP with Autogen", "toolkit": "LLMWhisperer", "toolkit_slug": "llmwhisperer", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/llmwhisperer/framework/autogen", "markdown_url": "https://composio.dev/toolkits/llmwhisperer/framework/autogen.md", "updated_at": "2026-03-29T06:40:48.248Z" } ``` ## Introduction This guide walks you through connecting LLMWhisperer to AutoGen using the Composio tool router. By the end, you'll have a working LLMWhisperer agent that can summarize main points from uploaded contract, extract key dates from legal document, classify sections of this technical report through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a LLMWhisperer account through Composio's LLMWhisperer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate LLMWhisperer with - [OpenAI Agents SDK](https://composio.dev/toolkits/llmwhisperer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/llmwhisperer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/llmwhisperer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/llmwhisperer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/llmwhisperer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/llmwhisperer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/llmwhisperer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/llmwhisperer/framework/cli) - [Google ADK](https://composio.dev/toolkits/llmwhisperer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/llmwhisperer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/llmwhisperer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/llmwhisperer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/llmwhisperer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/llmwhisperer/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 LLMWhisperer - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call LLMWhisperer tools - Run a live chat loop where you ask the agent to perform LLMWhisperer 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 LLMWhisperer MCP server, and what's possible with it? The LLMWhisperer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your LLMWhisperer account. It provides structured and secure access so your agent can perform LLMWhisperer operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `LLMWHISPERER_USAGE_GET_INFO` | Get Usage Information | Tool to check usage metrics of your LLMWhisperer account. Use when you need to monitor API consumption, verify quotas, or check remaining page limits. | | `LLMWHISPERER_USAGE_GET_STATS` | Get Usage Statistics | Tool to retrieve usage statistics for your LLMWhisperer account based on a specific tag. Use when you need to check consumption metrics for a given tag and optional date range. Returns usage data for the preceding 30 days when date parameters are omitted. | | `LLMWHISPERER_WEBHOOK_DELETE` | Delete Webhook | Tool to delete a registered webhook from LLMWhisperer system. Use when you need to remove a webhook that is no longer needed. | | `LLMWHISPERER_WEBHOOK_GET_DETAILS` | Get Webhook Details | Tool to retrieve registered webhook details for LLMWhisperer. Use when you need to get the configuration of a specific webhook including its URL and authentication token. | | `LLMWHISPERER_REGISTER_WEBHOOK` | Register Webhook | Tool to register a new webhook endpoint for LLMWhisperer async notifications. Use when you need to set up a callback URL to receive processing results. During registration, a test payload is sent to verify the webhook endpoint is functioning and returns HTTP 200. | | `LLMWHISPERER_UPDATE_WEBHOOK_CONFIGURATION` | Update Webhook Configuration | Tool to update an existing webhook configuration for document conversion callbacks. Use when you need to modify the callback URL, authentication token, or webhook identifier. The system validates the webhook by sending a test payload and requires a 200 status response. | | `LLMWHISPERER_CHECK_WHISPER_STATUS` | Check Whisper Status | Tool to check the status of a text extraction process in LLMWhisperer. Use when the conversion is done in async mode to poll for completion status. | | `LLMWHISPERER_CONVERT_DOCUMENT_TO_TEXT` | Convert Document to Text | Tool to convert PDFs and scanned documents into LLM-optimized text format asynchronously. Use when you need to extract text from documents for LLM processing. After submission, use the returned whisper_hash to poll status and retrieve converted text. Either document_url (with url_in_post=true) or document_content must be provided. | | `LLMWHISPERER_GET_WHISPER_DETAIL` | Get Whisper Detail | Tool to retrieve comprehensive details about ongoing or completed text extraction process. Use when you need to monitor the status and progress metrics of a text extraction job. | | `LLMWHISPERER_RETRIEVE_WHISPER_TEXT` | Retrieve Whisper Text | Tool to retrieve extracted text from asynchronous whisper processing. Use when the conversion process was initiated in async mode and you need to retrieve the results using the whisper_hash identifier. Note that retrieval is single-use for security - once retrieved, the same whisper_hash cannot be used again. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The LLMWhisperer MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to LLMWhisperer. Instead of manually wiring LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["llmwhisperer"] ) 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 LLMWhisperer 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 LLMWhisperer assistant agent with MCP tools agent = AssistantAgent( name="llmwhisperer_assistant", description="An AI assistant that helps with LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["llmwhisperer"] ) 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 LLMWhisperer assistant agent with MCP tools agent = AssistantAgent( name="llmwhisperer_assistant", description="An AI assistant that helps with LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer 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 LLMWhisperer, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build LLMWhisperer MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/llmwhisperer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/llmwhisperer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/llmwhisperer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/llmwhisperer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/llmwhisperer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/llmwhisperer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/llmwhisperer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/llmwhisperer/framework/cli) - [Google ADK](https://composio.dev/toolkits/llmwhisperer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/llmwhisperer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/llmwhisperer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/llmwhisperer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/llmwhisperer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/llmwhisperer/framework/crew-ai) ## Related Toolkits - [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices. - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [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. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze. - [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [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. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [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. - [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. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and LLMWhisperer MCP? With a standalone LLMWhisperer MCP server, the agents and LLMs can only access a fixed set of LLMWhisperer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from LLMWhisperer 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 LLMWhisperer tools. ### Can I manage the permissions and scopes for LLMWhisperer while using Tool Router? Yes, absolutely. You can configure which LLMWhisperer 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 LLMWhisperer data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)