How to integrate YouTube Transcript MCP with Pydantic AI

This guide walks you through connecting YouTube Transcript to Pydantic AI using the Composio tool router. By the end, you'll have a working YouTube Transcript agent that can fetch video transcripts, fetch transcripts in batches, list stored transcripts, and check transcript languages through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a YouTube Transcript account through Composio's YouTube Transcript MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Api Key

YouTube Transcript is a REST API for retrieving, storing, and reading YouTube video captions and transcript metadata. Use it to turn YouTube videos, playlists, and channels into searchable transcript data.

9 Tools

Introduction

This guide walks you through connecting YouTube Transcript to Pydantic AI using the Composio tool router. By the end, you'll have a working YouTube Transcript agent that can fetch video transcripts, fetch transcripts in batches, list stored transcripts, and check transcript languages through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a YouTube Transcript account through Composio's YouTube Transcript MCP server.

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

Also integrate YouTube Transcript with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for YouTube Transcript
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your YouTube Transcript workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the YouTube Transcript MCP server, and what's possible with it?

The YouTube Transcript 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 Transcript account. It provides structured and secure access to video captions, stored transcripts, processing statuses, available languages, and playlist or channel video details, so your agent can retrieve captions, monitor transcription work, search saved transcripts, inspect language options, and find videos from playlists or channels on your behalf.

  • Single video transcription: Have your agent fetch captions for a YouTube video and save the resulting transcript to your account.
  • Batch caption retrieval: Direct your agent to retrieve and store captions for a group of up to 10 YouTube videos.
  • Transcription status checks: Let the agent check whether individual or batch transcription work is processing, complete, failed, or waiting for speech recognition confirmation.
  • Stored transcript access: Instruct your agent to list, search, and read transcripts already saved in your account without starting new transcription work.
  • Language and video discovery: Have your agent review stored and translation languages for a transcript, or find video details from a YouTube playlist or channel.

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active 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

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like YouTube Transcript
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to YouTube Transcript
  • MCPServerStreamableHTTP connects to the YouTube Transcript MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for YouTube Transcript
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["youtube_transcript"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to YouTube Transcript 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
youtube_transcript_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[youtube_transcript_mcp],
    instructions=(
        "You are a YouTube Transcript assistant. Use YouTube Transcript tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the YouTube Transcript endpoint
  • The agent uses GPT-5 to interpret user commands and perform YouTube Transcript operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with YouTube Transcript.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • YouTube Transcript API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with YouTube Transcript and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for YouTube Transcript
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["youtube_transcript"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    youtube_transcript_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[youtube_transcript_mcp],
        instructions=(
            "You are a YouTube Transcript assistant. Use YouTube Transcript tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with YouTube Transcript.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with YouTube Transcript through Composio's Tool Router. With this setup, your agent can perform real YouTube Transcript actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + YouTube Transcript for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

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

Get Stored Transcript

Read a transcript already stored in the connected account without submitting new transcription work or consuming retrieval credits.

Get Video Transcript

Fetch captions for one YouTube video and store the resulting transcript in the connected account.

Get Video Transcript Job Status

Read one transcript job's current processing, completed, failed, or requires-ASR-confirmation state without creating work or waiting.

Get Video Transcripts Batch

Fetch and store captions for 1-10 YouTube videos.

Get Video Transcripts Batch Status

Read the current state and available per-video results for a transcript batch without creating work or waiting.

List Stored Transcripts

List and search transcripts already stored in the connected account, one bounded page at a time.

List Transcript Languages

List stored languages and YouTube translation target languages for one owned video transcript.

Resolve Channel Videos

Requires Basic or higher.

Resolve Playlist Videos

Requires Basic or higher.

FRAMEWORKS

How to build YouTube Transcript MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use YouTube Transcript MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use YouTube Transcript MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use YouTube Transcript MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use YouTube Transcript MCP with Claude Cowork

Codex logo

Codex

Use YouTube Transcript MCP with Codex

OpenClaw logo

OpenClaw

Use YouTube Transcript MCP with OpenClaw

Hermes logo

Hermes

Use YouTube Transcript MCP with Hermes

CLI logo

CLI

Use YouTube Transcript MCP with CLI

Google ADK logo

Google ADK

Use YouTube Transcript MCP with Google ADK

LangChain logo

LangChain

Use YouTube Transcript MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use YouTube Transcript MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use YouTube Transcript MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use YouTube Transcript MCP with LlamaIndex

CrewAI logo

CrewAI

Use YouTube Transcript MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Pydantic AI 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 Transcript tools.

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

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