How to integrate V0 MCP with LangChain

This guide walks you through connecting V0 to LangChain 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 LangChain 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.

V0 logoV0
Api Key

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 LangChain 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 LangChain 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.

Also integrate V0 with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your V0 project to Composio
  • Create a Tool Router MCP session for V0
  • Initialize an MCP client and retrieve V0 tools
  • Build a LangChain agent that can interact with V0
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with V0 functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to V0 tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['v0']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to V0 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
  • This approach allows the agent to dynamically load and use V0 tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "v0-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our V0 MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available V0 tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any V0 related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['v0']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "v0-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any V0 related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with V0 through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
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. LangChain 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.

Start with V0.It takes 30 seconds.

Managed auth, hosted MCP servers, and every V0 tool your agent needs.Free to start.

Start building