# How to integrate La Growth Machine MCP with Pydantic AI ```json { "title": "How to integrate La Growth Machine MCP with Pydantic AI", "toolkit": "La Growth Machine", "toolkit_slug": "lagrowthmachine", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/lagrowthmachine/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/lagrowthmachine/framework/pydantic-ai.md", "updated_at": "2026-03-29T06:39:56.150Z" } ``` ## Introduction This guide walks you through connecting La Growth Machine to Pydantic AI using the Composio tool router. By the end, you'll have a working La Growth Machine agent that can launch a multi-channel campaign for new leads, get outreach stats for this week's campaigns, add a contact to your sales pipeline through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a La Growth Machine account through Composio's La Growth Machine MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate La Growth Machine with - [OpenAI Agents SDK](https://composio.dev/toolkits/lagrowthmachine/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/lagrowthmachine/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/lagrowthmachine/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/lagrowthmachine/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/lagrowthmachine/framework/codex) - [OpenClaw](https://composio.dev/toolkits/lagrowthmachine/framework/openclaw) - [Hermes](https://composio.dev/toolkits/lagrowthmachine/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/lagrowthmachine/framework/cli) - [Google ADK](https://composio.dev/toolkits/lagrowthmachine/framework/google-adk) - [LangChain](https://composio.dev/toolkits/lagrowthmachine/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/lagrowthmachine/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/lagrowthmachine/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/lagrowthmachine/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/lagrowthmachine/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 La Growth Machine - 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 La Growth Machine 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 La Growth Machine MCP server, and what's possible with it? The La Growth Machine MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your La Growth Machine account. It provides structured and secure access so your agent can perform La Growth Machine operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `LAGROWTHMACHINE_ADD_RB2B_VISITOR_TO_AUDIENCE` | Add RB2B Visitor to Audience | Tool to add RB2B website visitor to a LaGrowthMachine audience via native webhook. Use when you need to push identified website visitors from RB2B into a specific audience for outreach campaigns. | | `LAGROWTHMACHINE_CREATE_AUDIENCE_FROM_LINKEDIN_URL` | Create Audience from LinkedIn URL | Tool to import leads into La Growth Machine audiences from LinkedIn URLs. Accepts LinkedIn Regular search URLs, Sales Navigator search URLs, or LinkedIn post URLs. Requires specifying the identity to impersonate and the audience name to populate. | | `LAGROWTHMACHINE_CREATE_INBOX_WEBHOOK` | Create Inbox Webhook | Tool to create an inbox webhook for real-time notifications. Use when you need to receive notifications about LinkedIn and Email messages sent or received by leads in La Growth Machine campaigns. | | `LAGROWTHMACHINE_CREATE_OR_UPDATE_LEAD` | Create or Update Lead | Tool to create or update a lead in LaGrowthMachine. Requires audience plus at least one identifier: proEmail, persoEmail, linkedinUrl, twitter, or firstname+lastname with companyUrl/companyName. Use leadId to update an existing lead. | | `LAGROWTHMACHINE_DELETE_INBOX_WEBHOOK` | Delete Inbox Webhook | Tool to delete an existing inbox webhook by its ID. Use when you need to stop receiving inbox events at the webhook URL. | | `LAGROWTHMACHINE_GET_CAMPAIGNS` | Get Campaigns | Tool to retrieve all campaigns from LaGrowthMachine with pagination support. Use when you need to list campaigns, with optional skip and limit parameters for pagination (max 25 per page). | | `LAGROWTHMACHINE_LIST_AUDIENCES` | List Audiences | Tool to list all audiences in your LaGrowthMachine account. Use when you need to retrieve audience details including ID, name, description, size, type, and source URL. | | `LAGROWTHMACHINE_LIST_IDENTITIES` | List Identities | Tool to list all connected identities in your LaGrowthMachine account. Use when you need to retrieve identity IDs for sending LinkedIn or Email messages through other APIs. | | `LAGROWTHMACHINE_LIST_INBOX_WEBHOOKS` | List Inbox Webhooks | Tool to list all inbox webhooks currently configured in your workspace. Use when you need to retrieve webhook IDs, names, and target URLs for webhook management or audit purposes. | | `LAGROWTHMACHINE_LIST_MEMBERS` | List Members | Tool to list all members (users) associated with your workspace. Use when you need to retrieve member information, especially memberId which is required for action-based endpoints like sending LinkedIn or Email messages. | | `LAGROWTHMACHINE_REGISTER_VECTOR_VISITOR_WEBHOOK` | Register Vector Visitor Webhook | Tool to register Vector website visitors to a La Growth Machine audience. Use when receiving visitor events from Vector integration to automatically add identified contacts to the specified audience. | | `LAGROWTHMACHINE_REMOVE_LEAD_FROM_AUDIENCES` | Remove Lead From Audiences | Tool to remove a lead from one or more specified audiences in La Growth Machine. Use when you need to unsubscribe or remove a lead from audience lists. | | `LAGROWTHMACHINE_SEARCH_LEAD` | Search Lead | Tool to search for a lead using various criteria. Use when you need to find a lead by email, LinkedIn URL, lead ID, or name combination. At least one of these must be provided: email, linkedinUrl, leadId, or firstname+lastname+(companyName or companyUrl). | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The La Growth Machine MCP server is an implementation of the Model Context Protocol that connects your AI agent to La Growth Machine. It provides structured and secure access so your agent can perform La Growth Machine 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 La Growth Machine - 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 La Growth Machine - MCPServerStreamableHTTP connects to the La Growth Machine 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 La Growth Machine 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 La Growth Machine composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["lagrowthmachine"], ) 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 La Growth Machine endpoint - The agent uses GPT-5 to interpret user commands and perform La Growth Machine operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent lagrowthmachine_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[lagrowthmachine_mcp], instructions=( "You are a La Growth Machine assistant. Use La Growth Machine 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 - La Growth Machine 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 La Growth Machine.\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 La Growth Machine composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["lagrowthmachine"], ) 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 lagrowthmachine_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[lagrowthmachine_mcp], instructions=( "You are a La Growth Machine assistant. Use La Growth Machine 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 La Growth Machine.\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 La Growth Machine through Composio's Tool Router. With this setup, your agent can perform real La Growth Machine 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 + La Growth Machine 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 La Growth Machine MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/lagrowthmachine/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/lagrowthmachine/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/lagrowthmachine/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/lagrowthmachine/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/lagrowthmachine/framework/codex) - [OpenClaw](https://composio.dev/toolkits/lagrowthmachine/framework/openclaw) - [Hermes](https://composio.dev/toolkits/lagrowthmachine/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/lagrowthmachine/framework/cli) - [Google ADK](https://composio.dev/toolkits/lagrowthmachine/framework/google-adk) - [LangChain](https://composio.dev/toolkits/lagrowthmachine/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/lagrowthmachine/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/lagrowthmachine/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/lagrowthmachine/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/lagrowthmachine/framework/crew-ai) ## Related Toolkits - [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. - [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. - [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. - [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. 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It helps you build flexible automations with complex conditions, schedules, and integrations. - [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place. - [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Basin](https://composio.dev/toolkits/basin) - Basin is a no-code form backend for quickly setting up reliable contact forms. It lets you collect and manage form submissions without writing any server-side code. - [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives. - [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates. - [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and La Growth Machine MCP? With a standalone La Growth Machine MCP server, the agents and LLMs can only access a fixed set of La Growth Machine tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from La Growth Machine 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 La Growth Machine tools. ### Can I manage the permissions and scopes for La Growth Machine while using Tool Router? Yes, absolutely. You can configure which La Growth Machine 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 La Growth Machine data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)