How to integrate Youtube MCP with LangChain

This guide walks you through connecting Youtube to LangChain using the Composio tool router. By the end, you'll have a working Youtube agent that can list your most recent uploaded videos, get subscriber count for your channel, search youtube for trending tutorials through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Youtube account through Composio's Youtube MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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YouTube is a leading video-sharing platform for uploading, streaming, and discovering content. It empowers creators and businesses to reach global audiences and monetize their work.

47 Tools4 Triggers

Introduction

This guide walks you through connecting Youtube to LangChain using the Composio tool router. By the end, you'll have a working Youtube agent that can list your most recent uploaded videos, get subscriber count for your channel, search youtube for trending tutorials through natural language commands.

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

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

Also integrate Youtube with

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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Youtube project to Composio
  • Create a Tool Router MCP session for Youtube
  • Initialize an MCP client and retrieve Youtube tools
  • Build a LangChain agent that can interact with Youtube
  • 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 Youtube MCP server, and what's possible with it?

The Youtube MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Youtube account. It provides structured and secure access to your channel data, so your agent can perform actions like searching videos, managing playlists, retrieving channel insights, and handling subscriptions on your behalf.

  • Channel activity monitoring: Let your agent fetch and summarize recent channel activities, including uploads, likes, playlist additions, and more, to keep you up to date at a glance.
  • Automated video and playlist management: Easily list videos from any channel, retrieve your own playlists, and organize your content—all through AI-driven commands.
  • Channel analytics and statistics: Ask your agent to pull detailed channel metrics such as subscriber counts, total views, or video counts for quick reporting and insights.
  • Subscription management: Have your agent list your current subscriptions or even subscribe you to new channels based on your interests or instructions.
  • Search and caption handling: Empower your agent to search YouTube for videos, channels, or playlists, as well as retrieve and download caption tracks for accessible viewing and content repurposing.

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 Youtube 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 Youtube 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: ['youtube']
    }
);

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

Configure the agent with the MCP URL

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

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "youtube-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 Youtube 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 Youtube 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 & TRIGGERS

Supported Tools and Triggers

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

Add Video to Playlist

Tool to add a video to a playlist by inserting a playlist item.

Insert Channel Section

Tool to create a new channel section for the authenticated user's YouTube channel.

Insert Comment Reply

Tool to create a reply to an existing YouTube comment.

Create Playlist

Tool to create a new YouTube playlist on the authenticated user's channel.

Delete Channel Section

Tool to delete a YouTube channel section.

Delete Comment

Tool to delete a YouTube comment owned by the authenticated user or channel.

Delete Playlist

Tool to delete a YouTube playlist owned by the authenticated user/channel.

Delete Playlist Item

Tool to delete a playlist item (remove a video from a playlist).

Delete Video

Tool to delete a YouTube video owned by the authenticated user/channel.

Get Channel Activities

Gets recent activities from a YouTube channel including video uploads, playlist additions, likes, and other channel events.

Get channel ID by handle

Retrieves the YouTube Channel ID for a specific YouTube channel handle.

Get Channel Statistics

Gets detailed statistics for YouTube channels including subscriber counts, view counts, and video counts.

Video Details Batch

Retrieves multiple YouTube video resource parts in a single batch call.

Get Video Rating

Retrieves the ratings that the authorized user gave to a list of specified videos.

List captions

Retrieves a list of caption tracks for a YouTube video.

List Channel Sections

Tool to retrieve channel sections from YouTube.

List channel videos

Lists videos from a specified YouTube channel.

List Comments

List individual comments from YouTube videos.

List Comment Threads

Tool to retrieve comment threads from YouTube videos or channels matching API request parameters.

List I18n Languages

Returns a list of application languages that the YouTube website supports.

List I18n Regions

Tool to retrieve a list of content regions that the YouTube website supports.

List Live Chat Messages

Tool to list live chat messages for a specific chat.

List Playlist Images

Tool to retrieve playlist images associated with a specific playlist.

List Playlist Items

Tool to list videos in a playlist, with pagination support.

List Super Chat Events

Lists Super Chat events for a channel, showing supporter purchases during live streams.

List user playlists

Retrieves playlists owned by the authenticated user, implicitly using mine=True.

List user subscriptions

Retrieves the authenticated user's YouTube channel subscriptions, allowing specification of response parts and pagination.

List Video Abuse Report Reasons

Tool to retrieve a list of abuse report reasons that can be used to report abusive videos on YouTube.

List Video Categories

Tool to list YouTube video categories that can be associated with videos.

Download YouTube caption track

Downloads a specific YouTube caption track, which must be owned by the authenticated user, and returns its content as text.

Multipart upload video

Uploads a video to YouTube using multipart upload in a single request.

Post Comment on Video

Tool to post a new top-level comment on a YouTube video.

Rate Video

Tool to add a like or dislike rating to a YouTube video, or remove an existing rating.

Report Video for Abuse

Tool to report a YouTube video for containing abusive content.

Search YouTube

Searches YouTube for videos, channels, or playlists using a query term, returning the raw API response.

Set Comment Moderation Status

Tool to set the moderation status of one or more YouTube comments.

Subscribe to channel

Subscribes the authenticated user to a specified YouTube channel, identified by its unique `channelId` which must be valid and existing.

Unsubscribe from channel

Tool to unsubscribe the authenticated user from a YouTube channel by deleting a subscription.

Update caption track

Updates a YouTube caption track's metadata such as name, language, or draft status.

Update channel

Updates a channel's metadata including branding settings and localizations.

Update Channel Section

Tool to update an existing YouTube channel section by ID.

Update Comment

Tool to modify the text of an existing YouTube comment.

Update Playlist

Tool to modify an existing YouTube playlist's metadata (title, description, privacy status).

Update Playlist Item

Tool to modify a playlist item's properties such as position or note.

Update thumbnail

Sets the custom thumbnail for a YouTube video using an image from a URL.

Update video

Updates metadata for a YouTube video identified by videoId, which must exist; an empty list for tags removes all existing tags.

Upload video

Uploads a video from a local file path to a YouTube channel; the video file must be in a YouTube-supported format.

FRAMEWORKS

How to build Youtube MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Youtube MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Youtube MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Youtube MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Youtube MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Youtube MCP with Claude Cowork

Codex logo

Codex

Use Youtube MCP with Codex

Cursor logo

Cursor

Use Youtube MCP with Cursor

OpenClaw logo

OpenClaw

Use Youtube MCP with OpenClaw

Hermes logo

Hermes

Use Youtube MCP with Hermes

CLI logo

CLI

Use Youtube MCP with CLI

Google ADK logo

Google ADK

Use Youtube MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Youtube MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Youtube MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Youtube MCP with LlamaIndex

CrewAI logo

CrewAI

Use Youtube MCP with CrewAI

OpenCode logo

OpenCode

Use Youtube MCP with OpenCode

VS Code logo

VS Code

Use Youtube MCP with VS Code

FAQ

Frequently asked questions

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

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

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