# How to integrate La Growth Machine MCP with Autogen ```json { "title": "How to integrate La Growth Machine MCP with Autogen", "toolkit": "La Growth Machine", "toolkit_slug": "lagrowthmachine", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/lagrowthmachine/framework/autogen", "markdown_url": "https://composio.dev/toolkits/lagrowthmachine/framework/autogen.md", "updated_at": "2026-03-29T06:39:56.150Z" } ``` ## Introduction This guide walks you through connecting La Growth Machine to AutoGen 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 AutoGen 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: - 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 La Growth Machine - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call La Growth Machine tools - Run a live chat loop where you ask the agent to perform La Growth Machine 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 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 agents and assistants directly to La Growth Machine. Instead of manually wiring La Growth Machine 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 La Growth Machine 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 La Growth Machine 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 La Growth Machine 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 La Growth Machine 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 La Growth Machine session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["lagrowthmachine"] ) 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 La Growth Machine 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 La Growth Machine assistant agent with MCP tools agent = AssistantAgent( name="lagrowthmachine_assistant", description="An AI assistant that helps with La Growth Machine 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 La Growth Machine 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 La Growth Machine 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 La Growth Machine session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["lagrowthmachine"] ) 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 La Growth Machine assistant agent with MCP tools agent = AssistantAgent( name="lagrowthmachine_assistant", description="An AI assistant that helps with La Growth Machine 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 La Growth Machine 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 La Growth Machine 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 La Growth Machine, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## 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 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 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 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)