# How to integrate V0 MCP with LlamaIndex

```json
{
  "title": "How to integrate V0 MCP with LlamaIndex",
  "toolkit": "V0",
  "toolkit_slug": "v0",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/v0/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/v0/framework/llama-index.md",
  "updated_at": "2026-05-12T10:29:38.327Z"
}
```

## Introduction

This guide walks you through connecting V0 to LlamaIndex 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 LlamaIndex 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

- [ChatGPT](https://composio.dev/toolkits/v0/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/v0/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/v0/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/v0/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/v0/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/v0/framework/codex)
- [Cursor](https://composio.dev/toolkits/v0/framework/cursor)
- [VS Code](https://composio.dev/toolkits/v0/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/v0/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/v0/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/v0/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/v0/framework/cli)
- [Google ADK](https://composio.dev/toolkits/v0/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/v0/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/v0/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/v0/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/v0/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Set your OpenAI and Composio API keys
- Install LlamaIndex and Composio packages
- Create a Composio Tool Router session for V0
- Connect LlamaIndex to the V0 MCP server
- Build a V0-powered agent using LlamaIndex
- Interact with V0 through natural language

## What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.
Key features include:
- ReAct Agent: Reasoning and acting pattern for tool-using agents
- MCP Tools: Native support for Model Context Protocol
- Context Management: Maintain conversation context across interactions
- Async Support: Built for async/await patterns

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `V0_ASSIGN_CHAT_TO_PROJECT` | Assign Chat To Project | Tool to assign a chat to a project. Use when you need to group a conversation under a shared project context for better organization. |
| `V0_V0_CHAT_COMPLETIONS` | V0 Chat Completions | Tool to generate a chat model response given a list of messages. Use when you need AI-powered conversational replies via the v0 API. Note: This action uses the POST /chats endpoint which creates a persistent chat session in the user's v0 account. Each call will create a new chat that can be viewed at the returned webUrl or accessed via the apiUrl. |
| `V0_CREATE_HOOK_V0` | Create Webhook | Tool to create a new webhook subscription for receiving event notifications. Use when you need to listen for chat or message events from v0. |
| `V0_CREATE_PROJECT` | Create V0 Project | Tool to create a new v0 project container for chats and code generation. Use when you need to start a clean project with specific configuration. |
| `V0_CREATE_PROJECT_ENV_VARS` | Create Project Environment Variables | Tool to create new environment variables for a v0 project. Use when you need to add environment variables to a project. By default, fails if any keys already exist unless upsert is set to true. |
| `V0_CREATE_VERCEL_PROJECT` | Create Vercel Project | Tool to link a Vercel project to an existing v0 project. Use when you need to enable Vercel-related features and deployment integration for a v0 project. |
| `V0_DELETE_CHAT` | Delete Chat | Tool to permanently delete a specific chat by ID. Use when you need to remove a chat and all its contents irreversibly. |
| `V0_DELETE_DEPLOYMENT` | Delete Deployment | Tool to delete a deployment by ID from Vercel. Use when you need to remove a specific deployment permanently. |
| `V0_DELETE_HOOK` | Delete Hook | Tool to delete a webhook by its ID. Use when you need to remove a webhook subscription. This action is irreversible. |
| `V0_DELETE_PROJECT_ENV_VARS_V0` | Delete Project Environment Variables | Tool to delete multiple environment variables from a project by their IDs. Use when you need to remove environment variables from a v0 project. |
| `V0_DELETE_V0_PROJECT` | Delete V0 Project | Tool to permanently delete a v0 project by its ID. Use when you need to remove a project and optionally all its associated chats. This operation is irreversible. |
| `V0_DEPLOY_PROJECT` | Deploy Project | Tool to deploy a specific v0 chat version to Vercel. Use when you need to create a live deployment with public URLs after generating code via v0. |
| `V0_DOWNLOAD_CHAT_VERSION` | Download Chat Version | Tool to download all files for a specific chat version as a zip or tarball archive. Use when you need to retrieve a complete downloadable archive of a version's files, optionally including deployment configuration files. |
| `V0_EXPORT_PROJECT_CODE` | Export Project Code | Tool to export a deployable snapshot of a v0 chat version by retrieving all files (including default/deployment files). Use when you need to get the complete generated code for a specific version, ready for local deployment or artifact creation. |
| `V0_FAVORITE_CHAT` | Favorite Chat | Tool to mark a chat as favorite or remove the favorite status. Use when you need to organize and quickly access important chats. |
| `V0_V0_FIND_CHATS` | Find Chats | Tool to retrieve a list of chats. Use when you need to list user chats with pagination and optional favorite filtering after authentication. |
| `V0_V0_FIND_PROJECTS` | Find Projects | Tool to retrieve a list of projects associated with the authenticated user. Use after obtaining a valid API key. |
| `V0_FIND_VERCEL_PROJECTS` | Find Vercel Projects | Tool to retrieve a list of Vercel projects linked to the user's v0 workspace. Use when you need to discover available Vercel projects for deployment or integration purposes. |
| `V0_FORK_CHAT` | Fork Chat | Tool to create a fork (copy) of an existing chat. Use when you need to explore alternative directions without modifying the original conversation. |
| `V0_GET_CHAT` | Get Chat | Tool to retrieve the full details of a specific chat using its chatId. Use when you need to access chat messages, metadata, and configuration for a specific chat. |
| `V0_GET_CHAT_PROJECT` | Get Chat Project | Tool to retrieve the v0 project associated with a given chat. Use when you need to determine the context or scope of a chat session. |
| `V0_GET_DEPLOYMENT_ERRORS` | Get Deployment Errors | Tool to retrieve errors for a specific deployment. Use when diagnosing and debugging deployment issues. |
| `V0_GET_DEPLOYMENT_LOGS` | Get Deployment Logs | Tool to retrieve logs for a specific deployment. Use when you need to debug or monitor deployment behavior by fetching log entries, optionally filtered by timestamp. |
| `V0_GET_HOOK` | Get Hook | Tool to retrieve detailed information about a specific webhook by its ID. Use when you need to inspect webhook configuration, subscribed events, or target URL. |
| `V0_GET_MESSAGE` | Get Chat Message | Tool to retrieve detailed information about a specific message within a chat. Use when you need to access message content, metadata, attachments, or model configuration for a known message ID. |
| `V0_GET_PROJECT` | Get Project by ID | Tool to retrieve the details of a specific v0 project by its ID, including associated chats and metadata. Use when you need to inspect project configuration or access related chats. |
| `V0_GET_PROJECT_ENV_VAR` | Get Project Environment Variable | Tool to retrieve a specific environment variable for a given project by its ID, including its value. Use when you need to get the details of a particular environment variable for a project. |
| `V0_GET_RATE_LIMITS` | Get Rate Limits | Tool to retrieve the current rate limits for the authenticated user. Use when you need to monitor usage limits and avoid throttling. |
| `V0_GET_USAGE_REPORT_V0` | Get Usage Report | Tool to retrieve detailed usage events including costs, models used, and metadata. Use when you need to access usage data from the dashboard, filter by chat/user, or analyze costs. |
| `V0_GET_USER` | Get User | Tool to retrieve the currently authenticated user's information. Use when you need to get the user's ID, name, email, avatar, or account metadata. |
| `V0_GET_USER_BILLING` | Get User Billing | Tool to fetch billing usage and quota information for the authenticated user. Use when you need to check the user's billing details or usage metrics. Can be scoped to a specific project or namespace. |
| `V0_GET_USER_PLAN_V0` | Get User Plan | Tool to retrieve the authenticated user's subscription plan details including billing cycle and balance. Use to check plan status and usage limits. |
| `V0_GET_USER_SCOPES` | Get User Scopes | Tool to retrieve all accessible scopes for the authenticated user, such as personal workspaces or shared teams. Use when you need to understand which workspaces the user can access. |
| `V0_INIT_V0_CHAT` | Initialize Chat | Tool to initialize a new chat from source content such as files, repositories, registries, zip archives, or templates. Use when you need to start a new v0 chat session with pre-populated content. Initialization uses no tokens. |
| `V0_LIST_CHAT_VERSIONS` | List Chat Versions | Tool to retrieve all versions (iterations) for a specific chat, ordered by creation date (newest first). Use when you need to view the history of a chat's generated versions with cursor-based pagination support. |
| `V0_LIST_DEPLOYMENTS` | List Deployments | Tool to retrieve a list of deployments for a given project, chat, and version. Use when you need to check existing deployments for specific project versions. |
| `V0_LIST_HOOKS` | List Hooks | Tool to retrieve all webhooks tied to chat events or deployments. Use when you need to list configured webhooks after authentication. |
| `V0_LIST_MESSAGES` | List Messages | Tool to retrieve all messages within a specific chat. Use when you need to list messages with content, role, and type information. Supports pagination for chats with many messages. |
| `V0_LIST_PROJECT_ENV_VARS` | List Project Environment Variables | Tool to retrieve all environment variables for a project with optional decryption. Use when you need to view project configuration or secrets. |
| `V0_UPDATE_CHAT` | Update Chat | Tool to update metadata of an existing v0 chat. Use when you need to rename a chat or change its privacy setting after creation. |
| `V0_UPDATE_CHAT_VERSION` | Update Chat Version Files | Tool to update source files of a specific chat version. Use when you need to manually edit generated files through the API. |
| `V0_UPDATE_HOOK` | Update V0 Webhook | Tool to update the configuration of an existing webhook, including its name, event subscriptions, or target URL. Use when you need to modify webhook settings after creation. |
| `V0_UPDATE_PROJECT` | Update V0 Project | Tool to update the metadata of an existing v0 project using its projectId. Use when you need to change the project name, description, instructions, or privacy setting. |
| `V0_UPDATE_PROJECT_ENV_VARS` | Update Project Environment Variables | Tool to update environment variables for a v0 project. Use when you need to modify the values of existing environment variables. |

## Supported Triggers

None listed.

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

The V0 MCP server is an implementation of the Model Context Protocol that connects your AI agent to V0. It provides structured and secure access so your agent can perform V0 operations on your behalf through a secure, permission-based interface.
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

Before you begin, make sure you have:
- Python 3.8/Node 16 or higher installed
- A Composio account with the API key
- An OpenAI API key
- A V0 account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and V0

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

Create a .env file in your project root:
These credentials will be used to:
- Authenticate with OpenAI's GPT-5 model
- Connect to Composio's Tool Router
- Identify your Composio user session for V0 access
```bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id
```

### 4. Import modules

No description provided.
```python
import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

What's happening here:
- We create a Composio client using your API key and configure it with the LlamaIndex provider
- We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, v0)
- The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
- LlamaIndex will connect to this endpoint to dynamically discover and use the available V0 tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["v0"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform V0 actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform V0 actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)
```

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["v0"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform V0 actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

What's happening here:
- We're orchestrating the entire application flow
- The agent gets built with proper error handling
- Then we kick off the interactive chat loop so you can start talking to V0
```python
async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with V0, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["v0"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform V0 actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform V0 actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["v0"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform V0 actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected V0 to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes V0 tools through an MCP endpoint
- LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
- The agent becomes more capable without increasing prompt size
- Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

## How to build V0 MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/v0/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/v0/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/v0/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/v0/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/v0/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/v0/framework/codex)
- [Cursor](https://composio.dev/toolkits/v0/framework/cursor)
- [VS Code](https://composio.dev/toolkits/v0/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/v0/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/v0/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/v0/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/v0/framework/cli)
- [Google ADK](https://composio.dev/toolkits/v0/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/v0/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/v0/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/v0/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/v0/framework/crew-ai)

## Related Toolkits

- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
- [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses.
- [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure.
- [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure.
- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
- [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting.
- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and V0 MCP?

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.

### Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex 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.

### Can I manage the permissions and scopes for V0 while using Tool Router?

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.

### 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 V0 data and credentials are handled as safely as possible.

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