How to integrate V0 MCP with Autogen

This guide walks you through connecting V0 to AutoGen using the Composio tool router. By the end, you'll have a working V0 agent that can generate react code for a login page, list all your active v0 projects, summarize our last five chat sessions through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a V0 account through Composio's V0 MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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V0 is an AI-powered web development assistant from Vercel that generates real, production-ready code for modern apps. Build modern web experiences faster with automated, intelligent code suggestions and UI components.

44 Tools

Introduction

This guide walks you through connecting V0 to AutoGen using the Composio tool router. By the end, you'll have a working V0 agent that can generate react code for a login page, list all your active v0 projects, summarize our last five chat sessions through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a V0 account through Composio's V0 MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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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 V0
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call V0 tools
  • Run a live chat loop where you ask the agent to perform V0 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 V0 MCP server, and what's possible with it?

The V0 MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your V0 account. It provides structured and secure access to your V0 projects and chat-powered workflows, so your agent can perform actions like generating code, managing web projects, retrieving chat histories, and facilitating AI-driven conversations on your behalf.

  • AI-powered chat completions: Instantly generate conversational replies or code suggestions using V0's advanced chat models tailored for web development workflows.
  • Retrieve and manage chat sessions: List and access your previous AI-assisted chat threads, including support for filtering favorites and paginated results.
  • Project discovery and management: Fetch a complete list of your web development projects, making it easy for your agent to interact with or summarize project data.
  • Integrated development automation: Seamlessly combine chat capabilities and project management to automate code generation, troubleshooting, or project setup tasks.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A V0 account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to V0 via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

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 V0 connections to use
5

Import dependencies and create Tool Router session

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 V0 session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["v0"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes V0 tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

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")}
)

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
7

Create the model client and agent

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 V0 assistant agent with MCP tools
    agent = AssistantAgent(
        name="v0_assistant",
        description="An AI assistant that helps with V0 operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the V0 tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any V0 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")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which V0 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

Complete Code

Here's the complete code to get you started with V0 and AutoGen:

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 V0 session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["v0"]
    )
    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 V0 assistant agent with MCP tools
        agent = AssistantAgent(
            name="v0_assistant",
            description="An AI assistant that helps with V0 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 V0 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 V0 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 V0, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every V0 action and event your agent gets out of the box.

Assign Chat To Project

Tool to assign a chat to a project.

V0 Chat Completions

Tool to generate a chat model response given a list of messages.

Create Webhook

Tool to create a new webhook subscription for receiving event notifications.

Create V0 Project

Tool to create a new v0 project container for chats and code generation.

Create Project Environment Variables

Tool to create new environment variables for a v0 project.

Create Vercel Project

Tool to link a Vercel project to an existing v0 project.

Delete Chat

Tool to permanently delete a specific chat by ID.

Delete Deployment

Tool to delete a deployment by ID from Vercel.

Delete Hook

Tool to delete a webhook by its ID.

Delete Project Environment Variables

Tool to delete multiple environment variables from a project by their IDs.

Delete V0 Project

Tool to permanently delete a v0 project by its ID.

Deploy Project

Tool to deploy a specific v0 chat version to Vercel.

Download Chat Version

Tool to download all files for a specific chat version as a zip or tarball archive.

Export Project Code

Tool to export a deployable snapshot of a v0 chat version by retrieving all files (including default/deployment files).

Favorite Chat

Tool to mark a chat as favorite or remove the favorite status.

Find Chats

Tool to retrieve a list of chats.

Find Projects

Tool to retrieve a list of projects associated with the authenticated user.

Find Vercel Projects

Tool to retrieve a list of Vercel projects linked to the user's v0 workspace.

Fork Chat

Tool to create a fork (copy) of an existing chat.

Get Chat

Tool to retrieve the full details of a specific chat using its chatId.

Get Chat Project

Tool to retrieve the v0 project associated with a given chat.

Get Deployment Errors

Tool to retrieve errors for a specific deployment.

Get Deployment Logs

Tool to retrieve logs for a specific deployment.

Get Hook

Tool to retrieve detailed information about a specific webhook by its ID.

Get Chat Message

Tool to retrieve detailed information about a specific message within a chat.

Get Project by ID

Tool to retrieve the details of a specific v0 project by its ID, including associated chats and metadata.

Get Project Environment Variable

Tool to retrieve a specific environment variable for a given project by its ID, including its value.

Get Rate Limits

Tool to retrieve the current rate limits for the authenticated user.

Get Usage Report

Tool to retrieve detailed usage events including costs, models used, and metadata.

Get User

Tool to retrieve the currently authenticated user's information.

Get User Billing

Tool to fetch billing usage and quota information for the authenticated user.

Get User Plan

Tool to retrieve the authenticated user's subscription plan details including billing cycle and balance.

Get User Scopes

Tool to retrieve all accessible scopes for the authenticated user, such as personal workspaces or shared teams.

Initialize Chat

Tool to initialize a new chat from source content such as files, repositories, registries, zip archives, or templates.

List Chat Versions

Tool to retrieve all versions (iterations) for a specific chat, ordered by creation date (newest first).

List Deployments

Tool to retrieve a list of deployments for a given project, chat, and version.

List Hooks

Tool to retrieve all webhooks tied to chat events or deployments.

List Messages

Tool to retrieve all messages within a specific chat.

List Project Environment Variables

Tool to retrieve all environment variables for a project with optional decryption.

Update Chat

Tool to update metadata of an existing v0 chat.

Update Chat Version Files

Tool to update source files of a specific chat version.

Update V0 Webhook

Tool to update the configuration of an existing webhook, including its name, event subscriptions, or target URL.

Update V0 Project

Tool to update the metadata of an existing v0 project using its projectId.

Update Project Environment Variables

Tool to update environment variables for a v0 project.

FAQ

Frequently asked questions

With a standalone V0 MCP server, the agents and LLMs can only access a fixed set of V0 tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from V0 and many other apps based on the task at hand, all through a single MCP endpoint.

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 V0 tools.

Yes, absolutely. You can configure which V0 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.

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 V0 data and credentials are handled as safely as possible.

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