# How to integrate Zylvie MCP with Autogen

```json
{
  "title": "How to integrate Zylvie MCP with Autogen",
  "toolkit": "Zylvie",
  "toolkit_slug": "zylvie",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/zylvie/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/zylvie/framework/autogen.md",
  "updated_at": "2026-05-06T08:35:15.299Z"
}
```

## Introduction

This guide walks you through connecting Zylvie to AutoGen using the Composio tool router. By the end, you'll have a working Zylvie agent that can add a new digital course product, fetch your current zylvie account profile, remove webhook for subscription cancellations through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Zylvie account through Composio's Zylvie MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Zylvie with

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

The Zylvie MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zylvie account. It provides structured and secure access to your Zylvie workspace, so your agent can perform actions like creating products, managing subscriptions, and retrieving user information on your behalf.
- Automated product creation: Instantly add new digital products to your Zylvie store with customizable options, all through natural language prompts to your agent.
- User profile retrieval: Quickly fetch details about the currently authenticated user, making it easy to personalize workflows and manage account settings.
- Webhook management: Seamlessly unsubscribe from webhooks by deleting workflow objects associated with specific webhook URLs, ensuring your integrations stay clean and up to date.
- Subscription and workflow automation: Let your agent help manage ongoing workflows by removing unneeded subscriptions, keeping your sales automations streamlined.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ZYLVIE_CREATE_PRODUCT` | Create Product | Tool to create a new product. use when you need to add a product with detailed custom options in zylvie platform. |
| `ZYLVIE_GET_AUTHENTICATING_USER` | Get Authenticating User | Tool to retrieve information about the currently authenticated user. use when you have a valid bearer token and need to fetch the authenticated user's profile. |
| `ZYLVIE_UNSUBSCRIBE_WEBHOOK` | Unsubscribe Webhook | Tool to unsubscribe from a webhook by deleting the workflow object associated with the specified webhook url. use after confirming the webhook url to remove. |

## Supported Triggers

None listed.

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

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

## How to build Zylvie MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/zylvie/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/zylvie/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/zylvie/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/zylvie/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/zylvie/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/zylvie/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/zylvie/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/zylvie/framework/cli)
- [Google ADK](https://composio.dev/toolkits/zylvie/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/zylvie/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/zylvie/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/zylvie/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/zylvie/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/zylvie/framework/crew-ai)

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- [Gift up](https://composio.dev/toolkits/gift_up) - Gift Up! is a digital platform for selling, managing, and redeeming gift cards online. It streamlines promotions and gift card transactions for businesses and their customers.
- [Goody](https://composio.dev/toolkits/goody) - Goody is a gifting platform that lets users send gifts and physical products without handling logistics. It streamlines gifting by managing delivery, fulfillment, and recipient experience.
- [Gumroad](https://composio.dev/toolkits/gumroad) - Gumroad is a platform for selling digital products, physical goods, and memberships with a simple checkout and marketing tools. It streamlines creator payouts and helps you grow your audience effortlessly.
- [Instacart](https://composio.dev/toolkits/instacart) - Instacart is an online grocery delivery and pickup service platform. It lets you discover local retailers and create shoppable lists and recipes with ease.
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## Frequently Asked Questions

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

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

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

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

---
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