# How to integrate LLMWhisperer MCP with Pydantic AI ```json { "title": "How to integrate LLMWhisperer MCP with Pydantic AI", "toolkit": "LLMWhisperer", "toolkit_slug": "llmwhisperer", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/llmwhisperer/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/llmwhisperer/framework/pydantic-ai.md", "updated_at": "2026-03-29T06:40:48.248Z" } ``` ## Introduction This guide walks you through connecting LLMWhisperer to Pydantic AI 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 Pydantic AI 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: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for LLMWhisperer - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your LLMWhisperer workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## 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 agent to LLMWhisperer. It provides structured and secure access so your agent can perform LLMWhisperer operations on your behalf through a secure, permission-based interface. 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 Before starting, make sure you have: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 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 the required libraries. What's happening: - composio connects your agent to external SaaS tools like LLMWhisperer - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to LLMWhisperer - MCPServerStreamableHTTP connects to the LLMWhisperer MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to LLMWhisperer tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for LLMWhisperer composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["llmwhisperer"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the LLMWhisperer endpoint - The agent uses GPT-5 to interpret user commands and perform LLMWhisperer operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent llmwhisperer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[llmwhisperer_mcp], instructions=( "You are a LLMWhisperer assistant. Use LLMWhisperer tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - LLMWhisperer API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with LLMWhisperer.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for LLMWhisperer composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["llmwhisperer"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent llmwhisperer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[llmwhisperer_mcp], instructions=( "You are a LLMWhisperer assistant. Use LLMWhisperer tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with LLMWhisperer.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with LLMWhisperer through Composio's Tool Router. With this setup, your agent can perform real LLMWhisperer actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + LLMWhisperer for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## 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 Pydantic AI? Yes, you can. Pydantic AI 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)