# How to integrate Engage MCP with Autogen

```json
{
  "title": "How to integrate Engage MCP with Autogen",
  "toolkit": "Engage",
  "toolkit_slug": "engage",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/engage/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/engage/framework/autogen.md",
  "updated_at": "2026-03-29T06:32:23.042Z"
}
```

## Introduction

This guide walks you through connecting Engage to AutoGen using the Composio tool router. By the end, you'll have a working Engage agent that can send sms to users about black friday deals, create and schedule a new email campaign, get analytics on last week's push notifications through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Engage account through Composio's Engage MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Engage with

- [OpenAI Agents SDK](https://composio.dev/toolkits/engage/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/engage/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/engage/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/engage/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/engage/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/engage/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/engage/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/engage/framework/cli)
- [Google ADK](https://composio.dev/toolkits/engage/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/engage/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/engage/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/engage/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/engage/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/engage/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 Engage
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Engage tools
- Run a live chat loop where you ask the agent to perform Engage 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 Engage MCP server, and what's possible with it?

The Engage MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Engage account. It provides structured and secure access so your agent can perform Engage operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ENGAGE_ADD_CUSTOMER_TO_ACCOUNTS` | Add Customer to Accounts | Tool to add a customer to one or more account entities. Use when you need to associate a user with accounts and optionally assign roles. |
| `ENGAGE_ADD_USER_TO_LISTS` | Add User to Lists | Tool to add a Customer or Account to one or more Lists in Engage.so. Use when you need to subscribe a user to specific lists for targeted messaging. |
| `ENGAGE_ARCHIVE_LIST` | Archive List | Tool to archive a List in Engage. Use when you want to prevent new subscribers from being added to a list. Existing subscribers will not be affected. |
| `ENGAGE_ARCHIVE_USER` | Archive User | Tool to archive a user in Engage. Use when you need to deactivate a user account while preserving all historical data. The user will stop being active and all engagement and events for the user will be stopped, but all messages, logs, and related data will be preserved. |
| `ENGAGE_CONVERT_USER_TYPE` | Convert User Type | Tool to convert a user between Customer and Account entity types. Use when you need to change a customer to an account or vice versa. |
| `ENGAGE_CREATE_LIST` | Create List | Tool to create a new List in Engage for organizing subscribers. Use when you need to set up a new list for managing contacts or subscribers. |
| `ENGAGE_CREATE_USER` | Create User | Tool to create a new user (Customer or Account) in Engage. Use when you need to add a user with optional metadata, device tokens, or list subscriptions. |
| `ENGAGE_DELETE_SUBSCRIBER_FROM_LIST` | Delete Subscriber From List | Tool to remove a subscriber from a List entirely (different from unsubscribing). Use when you need to completely delete a subscriber's association with a specific list. |
| `ENGAGE_DELETE_USER` | Delete User | Tool to completely delete all user data for a Customer or Account. This is a permanent, destructive action that removes all associated user data from Engage. |
| `ENGAGE_GET_ACCOUNT_MEMBERS` | Get Account Members | Tool to retrieve all members (Customers) of an Account in Engage. Use when you need to list users who are part of a specific account. |
| `ENGAGE_GET_LIST` | Get List | Tool to retrieve a single List by its ID. Use when you need to fetch details about a specific List. |
| `ENGAGE_GET_USER_BY_ID` | Get User By ID | Tool to retrieve a single user by their user ID. Use when you need to fetch complete user information including metadata, attributes, devices, lists, segments, and message statistics. |
| `ENGAGE_LIST_LISTS` | List Lists | Tool to retrieve a paginated list of all Lists in Engage. Use when you need to view available Lists or iterate through all Lists in the account. |
| `ENGAGE_LIST_USERS` | List Users | Tool to retrieve a paginated list of all users in Engage. Use when you need to list users with optional filtering by email and cursor-based pagination support. |
| `ENGAGE_MERGE_USERS` | Merge Users | Tool to merge two user profiles in Engage. The source user is merged into the destination user, and the source user profile is removed. Use when you need to consolidate duplicate user accounts or combine user data from multiple profiles into a single account. |
| `ENGAGE_REMOVE_CUSTOMER_FROM_ACCOUNT` | Remove Customer from Account | Tool to remove a Customer from an Account in Engage. Use when you need to disassociate a customer from a specific account. |
| `ENGAGE_BATCH_REQUEST` | Batch Request | Tool to batch multiple create user, update user, and add user events operations into a single API call. Use when you need to perform multiple operations efficiently at the cost of one API request. The batch is queued for processing without immediate validation, so ensure all parameters are correct. Request size must remain under 100KB. |
| `ENGAGE_SUBSCRIBE_USER_TO_LIST` | Subscribe User to List | Tool to create a user and subscribe them to an Engage.so List. Use when you need to add users to a specific list for email marketing or user segmentation. If the user already exists, they will be added to the List without creating a duplicate. |
| `ENGAGE_TRACK_USER_EVENT` | Track User Event | Tool to add user events to Engage. Use this to track user actions and events in your application. You can later segment users based on these actions or events. |
| `ENGAGE_UPDATE_ACCOUNT_ROLE` | Update Account Role | Tool to update the role of a Customer in an Account or set a new one if none exists. Use when you need to assign or change a customer's role within a specific account. |
| `ENGAGE_UPDATE_SUBSCRIBER_STATUS` | Update Subscriber Status | Tool to update a subscriber's status on a List. Use when you need to subscribe, re-subscribe, or unsubscribe a user from a specific List. |
| `ENGAGE_UPDATE_USER` | Update User | Tool to update user data and attributes on Engage. Use this to update user data changes like changes in plan, name, location, etc. If the user doesn't exist, this method creates the user. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Engage MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Engage. Instead of manually wiring Engage 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 Engage 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 Engage 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 Engage 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 Engage 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 Engage session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["engage"]
    )
    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 Engage 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 Engage assistant agent with MCP tools
    agent = AssistantAgent(
        name="engage_assistant",
        description="An AI assistant that helps with Engage 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 Engage 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 Engage 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 Engage session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["engage"]
    )
    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 Engage assistant agent with MCP tools
        agent = AssistantAgent(
            name="engage_assistant",
            description="An AI assistant that helps with Engage 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 Engage 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 Engage 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 Engage, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Engage MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/engage/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/engage/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/engage/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/engage/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/engage/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/engage/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/engage/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/engage/framework/cli)
- [Google ADK](https://composio.dev/toolkits/engage/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/engage/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/engage/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/engage/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/engage/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/engage/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. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing.
- [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages.
- [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections.
- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [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.
- [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.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
- [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns.
- [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.
- [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.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Engage MCP?

With a standalone Engage MCP server, the agents and LLMs can only access a fixed set of Engage tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Engage 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 Engage tools.

### Can I manage the permissions and scopes for Engage while using Tool Router?

Yes, absolutely. You can configure which Engage 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 Engage data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)