# ⚡ YouTube AI Transcript & Video Intelligence Extractor \[MCP] (`datalabs/youtube-ai-transcript-extractor-rag`) Actor

Extract YouTube video transcripts, timestamps, chapters, and metadata formatted as clean Markdown for AI RAG pipelines, LLMs, and Cursor.

- **URL**: https://apify.com/datalabs/youtube-ai-transcript-extractor-rag.md
- **Developed by:** [Jayshree Jain](https://apify.com/datalabs) (community)
- **Categories:** AI, Agents, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## ⚡ YouTube AI Transcript & Video Intelligence Extractor \[MCP]

> Extract YouTube video transcripts, timestamps, chapters, and metadata formatted as clean Markdown for AI RAG pipelines, LLMs, and Cursor.

***

### 🌟 Architectural Comparison: AI-Ready Transcript Structuring vs. Raw Subtitle Scrapers

| Feature | Traditional Browser-Based Scrapers | ⚡ YouTube AI Transcript Extractor \[MCP] |
| :--- | :--- | :--- |
| **RAG Chapter Chunking** | ❌ Dumps wall of text | ✅ **Automatic `## [MM:SS] Chapter` segmentation** |
| **Punctuation & Flow** | ❌ Raw fragmented ASR cues | ✅ **Punctuation-restored paragraphs** |
| **LLM Context Budgeting**| ❌ No token calculation | ✅ **Calculates characters, words, & estimated LLM tokens** |
| **Manual vs ASR Detection**| ❌ Random caption selection | ✅ **Prioritizes human captions over auto-generated ASR** |
| **Metadata & JSON-LD** | ❌ Text only | ✅ **Full VideoObject Schema.org, view count, & channel intel** |
| **Error Handling** | ❌ Crashes on un-captioned videos | ✅ **Graceful error taxonomy (Exit 0 guaranteed)** |
| **Execution Architecture** | Heavyweight Browser Emulation | ✅ **Direct TimedText HTTP Extraction (Zero Browser Overhead)** |

***

### 📥 Input Example

```json
{
  "startUrls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ],
  "preferManualCaptions": true,
  "targetLanguages": ["en", "en-US"],
  "includeChapters": true,
  "includeTimestamps": true,
  "includeMetadata": true
}
```

***

### 📤 Output Dataset Format

```json
{
  "videoId": "dQw4w9WgXcQ",
  "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "title": "Rick Astley - Never Gonna Give You Up (Official Music Video)",
  "channelName": "Rick Astley",
  "channelUrl": "https://www.youtube.com/channel/UCuAXFkgsw1L7xaCfnd5JJOw",
  "durationSeconds": 213,
  "durationFormatted": "03:33",
  "viewCount": 1814378375,
  "captionType": "manual",
  "language": "en",
  "chaptersCount": 4,
  "chapters": [
    { "title": "Intro", "startTimeSeconds": 0, "timestamp": "00:00" },
    { "title": "Chorus", "startTimeSeconds": 43, "timestamp": "00:43" }
  ],
  "transcriptMarkdown": "# Rick Astley - Never Gonna Give You Up\n\n## [00:00] Intro\n[00:01] [Music] ...",
  "charCount": 2771,
  "wordCount": 512,
  "estimatedTokens": 692,
  "success": true,
  "fetchedAt": "2026-09-11T10:15:00.000Z"
}
```

# Actor input Schema

## `startUrls` (type: `array`):

List of YouTube video URLs (e.g. https://www.youtube.com/watch?v=...), Shorts, youtu.be links, or raw 11-character video IDs.

## `preferManualCaptions` (type: `boolean`):

Prioritize human-created manual captions over auto-generated ASR captions when available.

## `targetLanguages` (type: `array`):

Language codes in priority order (e.g. \['en', 'en-US', 'es', 'de']).

## `includeChapters` (type: `boolean`):

Detect video chapters and format transcript into Markdown ## \[MM:SS] chapter sections for RAG chunking.

## `includeTimestamps` (type: `boolean`):

Include \[MM:SS] markers before transcript sentences.

## `includeMetadata` (type: `boolean`):

Extract view count, channel subscriber count, upload date, duration, keywords, and VideoObject JSON-LD schema.

## `proxyConfiguration` (type: `object`):

Optional proxy settings for high-volume extraction across multiple videos.

## Actor input object example

```json
{
  "startUrls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ],
  "preferManualCaptions": true,
  "targetLanguages": [
    "en",
    "en-US"
  ],
  "includeChapters": true,
  "includeTimestamps": true,
  "includeMetadata": true,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `results` (type: `string`):

Contains extracted YouTube video transcripts, timestamps, chapters, and metadata formatted as clean Markdown for AI RAG pipelines, LLMs, and Cursor.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": [
        "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("datalabs/youtube-ai-transcript-extractor-rag").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "startUrls": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"] }

# Run the Actor and wait for it to finish
run = client.actor("datalabs/youtube-ai-transcript-extractor-rag").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ]
}' |
apify call datalabs/youtube-ai-transcript-extractor-rag --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datalabs/youtube-ai-transcript-extractor-rag"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/B7V1YavJB2xJuNN1k/builds/qjloyNYpq0lSCFJfQ/openapi.json