How to integrate Vapi MCP with LangChain

This guide walks you through connecting Vapi to LangChain using the Composio tool router. By the end, you'll have a working Vapi agent that can start a new outbound call campaign, get transcript from the last agent call, pause all ongoing voice agent sessions through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Vapi account through Composio's Vapi MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Vapi is a voice AI platform for building, testing, and deploying conversational voice agents. It provides real-time responses and seamless integration for voice-driven applications.

42 Tools

Introduction

This guide walks you through connecting Vapi to LangChain using the Composio tool router. By the end, you'll have a working Vapi agent that can start a new outbound call campaign, get transcript from the last agent call, pause all ongoing voice agent sessions through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Vapi account through Composio's Vapi 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
  • Connect your Vapi project to Composio
  • Create a Tool Router MCP session for Vapi
  • Initialize an MCP client and retrieve Vapi tools
  • Build a LangChain agent that can interact with Vapi
  • 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 Vapi MCP server, and what's possible with it?

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

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 Vapi 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 Vapi 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: ['vapi']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Vapi 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 Vapi tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "vapi-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 Vapi MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Vapi 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 Vapi 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 Vapi 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: ['vapi']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "vapi-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 Vapi 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 Vapi 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 Vapi action and event your agent gets out of the box.

Update Assistant

Tool to update an existing Vapi assistant configuration.

List Calls

Tool to list calls from Vapi.

Delete Chat

Tool to delete a chat by its ID from Vapi.

Get Chat

Tool to fetch chat details by ID.

Create Analytics Queries

Tool to create and execute analytics queries on VAPI data.

Create Assistant

Tool to create a new Vapi assistant with specified transcriber, voice, and AI model configurations.

Create Eval

Tool to create an eval for testing conversation flows.

Create OpenAI Chat

Tool to create an OpenAI-compatible chat using the Vapi API.

Create Phone Number

Tool to create a phone number with Vapi.

Create Monitoring Policy

Tool to create a monitoring policy in VAPI.

Create Provider Resource

Tool to create an 11Labs pronunciation dictionary resource.

Create Scorecard

Tool to create a scorecard for observability and evaluation.

Delete Call

Tool to delete a call by its unique identifier.

Delete Eval

Tool to delete an eval by ID.

Delete Phone Number

Tool to delete a phone number from Vapi.

Get Eval

Tool to retrieve an eval by its ID.

Delete Eval Run

Tool to delete an eval run by its ID from Vapi.

Update Eval

Tool to update an existing eval in Vapi.

Get Assistant

Tool to retrieve a specific assistant by ID from Vapi.

Get Call

Tool to fetch call details by ID.

Get File

Tool to retrieve a file by its ID from Vapi.

Get Insights

Tool to retrieve insights from Vapi.

List Monitoring Policies

Tool to retrieve monitoring policies from Vapi.

Get Observability Scorecard

Tool to list observability scorecards with optional filtering and pagination.

List Provider Resources

Tool to list provider resources from Vapi.

List Structured Outputs

Tool to list structured outputs with optional filtering.

Get Insights

Tool to retrieve insights from VAPI.

List Assistants

Tool to list all assistants in your VAPI organization.

List Chats

Tool to retrieve a list of chat conversations from VAPI.

List Evals

Tool to retrieve a paginated list of evals from Vapi.

List Provider Resources

Tool to retrieve provider resources from Vapi (e.

Update Insight

Tool to update an existing insight configuration in VAPI.

Create Phone Number

Tool to create a phone number with VAPI.

List Scorecards

Tool to retrieve a paginated list of scorecards from Vapi.

Create Session

Tool to create a new session in Vapi.

List Sessions

Tool to retrieve a paginated list of sessions from VAPI.

List Structured Outputs

Tool to list structured outputs with optional filtering and pagination.

Get Tool

Tool to fetch tool details by ID.

Test Code Tool Execution

Tool to test TypeScript code execution in Vapi's code tool environment.

Update Tool

Tool to update an existing Vapi tool configuration.

Update Phone Number

Tool to update an existing phone number configuration in VAPI.

Upload File

Tool to upload a file to Vapi Knowledge Base.

FRAMEWORKS

How to build Vapi MCP Agent with another framework

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OpenAI Agents SDK

Use Vapi MCP with OpenAI Agents SDK

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Claude Agent SDK

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Claude Code

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Claude Cowork logo

Claude Cowork

Use Vapi MCP with Claude Cowork

Codex logo

Codex

Use Vapi MCP with Codex

OpenClaw logo

OpenClaw

Use Vapi MCP with OpenClaw

Hermes logo

Hermes

Use Vapi MCP with Hermes

CLI logo

CLI

Use Vapi MCP with CLI

Google ADK logo

Google ADK

Use Vapi MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Vapi MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Vapi MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Vapi MCP with LlamaIndex

CrewAI logo

CrewAI

Use Vapi MCP with CrewAI

FAQ

Frequently asked questions

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

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

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