yt-network-scraper

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A network-first YouTube video scraper that extracts metadata, transcripts, comments, and summaries from captured network payloads using headless Chrome and Selenium.

Unlike traditional DOM scrapers, this package opens a real browser via Selenium, captures network responses through Chrome DevTools performance logs, and parses YouTube's own JSON payloads (ytInitialPlayerResponse, ytInitialData, ytcfg). It then uses YouTube's innertube API for transcripts and comments. This approach is more resilient to UI changes and avoids brittle CSS selectors.

Why yt-network-scraper?

There are several excellent YouTube libraries on PyPI. Here is how yt-network-scraper compares to the most popular ones, so you can pick the right tool for your use case:

When to use yt-network-scraper

Use this package if you need:

• Comments — very few libraries extract YouTube comments. This one does, with full author info, likes, hearted/pinned status, and reply counts.
• Dislike counts — integrated with the Return YouTube Dislike API, which no other listed library provides.
• Automatic summaries — built-in extractive summarization of transcripts, so you get a quick text summary alongside the full transcript.
• Access-block detection — detects CAPTCHAs, consent walls, and sign-in challenges and reports them rather than silently failing or trying to bypass them.
• A real browser session — some videos require JavaScript rendering and network-level payload capture that pure HTTP libraries cannot access. This package captures Chrome DevTools performance logs to extract YouTube's own JSON payloads.
• A single, unified result object — metadata, transcript, comments, engagement, summary, and network diagnostics in one typed VideoResult with to_dict() for JSON serialization.

Use a different package if you need:

• Video downloading → use yt-dlp or pytube
• Search or channel browsing → use ytscrape or tubescrape
• Transcripts only (lightweight, no browser) → use youtube-transcript-api
• Async / high-throughput scraping → use ytscrape or tubescrape
• Playlist extraction → use yt-dlp, pytube, or tubescrape

Features

• Video metadata: title, description, views, channel info, publish/upload dates, duration, tags, thumbnail
• Transcripts / captions: via timedtext URLs or the innertube get_panel endpoint, with automatic fallback
• Comments: via the innertube next continuation API, with deduplication and pagination
• Dislike counts: from the Return YouTube Dislike API
• Extractive summaries: word-frequency-based summarization of transcript or description text
• Access-block detection: detects CAPTCHAs, consent walls, and sign-in challenges (does not bypass them)
• Structured data models: typed dataclasses with JSON serialization
• CLI: convenient command-line interface
• Configurable: timeout, retries, delays, comment limits, language preference

Installation

Option 1: pip (requires Chrome installed locally)

pip install yt-network-scraper

Prerequisites: Google Chrome must be installed. Selenium Manager will automatically fetch a matching ChromeDriver in recent Selenium versions.

Option 2: Docker (no Chrome installation needed)

No need to install Chrome, Python, or any dependencies — Docker handles everything:

# Build the image
docker build -t yt-network-scraper .

# Scrape a video and save output to ./output/result.json
docker run --rm -v "$(pwd)/output:/output" yt-network-scraper \
  video "https://youtu.be/ALyQ-c9_HBI" --comments 25 --pretty --out /output/result.json

Option 3: Docker Compose

# Build and run with docker compose
docker compose run --rm yt-network-scraper \
  video "https://youtu.be/ALyQ-c9_HBI" --comments 25 --pretty --out /output/result.json

The docker-compose.yml is included in the repo. Output files are saved to the ./output/ directory via a mounted volume.

Quick Start

from yt_network_scraper import YouTubeScraper, ScraperConfig

config = ScraperConfig(max_comments=50, transcript_language="en")

with YouTubeScraper(config) as scraper:
    result = scraper.get_video("dQw4w9WgXcQ")

    print(result.metadata.title)
    print(f"Views: {result.metadata.views}")
    print(f"Channel: {result.metadata.channel_name}")

    if result.transcript.available:
        print(f"Transcript: {result.transcript.text[:200]}")

    if result.summary.available:
        print(f"Summary: {result.summary.text}")

    for comment in result.comments:
        print(f"  {comment.author}: {comment.text}")

You can pass a full URL, a youtu.be link, a shorts URL, or a bare 11-character video ID:

scraper.get_video("https://www.youtube.com/watch?v=dQw4w9WgXcQ")
scraper.get_video("https://youtu.be/dQw4w9WgXcQ")
scraper.get_video("https://www.youtube.com/shorts/dQw4w9WgXcQ")
scraper.get_video("dQw4w9WgXcQ")

Batch Scraping (Multiple Videos Concurrently)

Scrape multiple videos in parallel — each video gets its own browser instance running in a thread pool. Failed videos are captured without stopping the batch:

from yt_network_scraper import YouTubeScraper, ScraperConfig

config = ScraperConfig(
    max_comments=25,
    max_workers=4,       # 4 concurrent Chrome instances
    batch_delay=2.0,     # 2s delay between starting each task
)

with YouTubeScraper(config) as scraper:
    batch = scraper.batch_scrape([
        "https://youtu.be/VIDEO1",
        "https://youtu.be/VIDEO2",
        "https://youtu.be/VIDEO3",
        "VIDEO_ID_4",
    ])

    print(f"Succeeded: {batch.succeeded}, Failed: {batch.failed}")
    print(f"Time: {batch.elapsed_seconds}s")

    for result in batch.results:
        print(f"  {result.video_id}: {result.metadata.title}")

    for err in batch.errors:
        print(f"  FAILED: {err.url_or_id} — {err.error_message}")

With a progress callback (useful for long batches):

def progress(idx, total, video_id, status):
    print(f"  [{idx}/{total}] {status.upper():5s} — {video_id}")

batch = scraper.batch_scrape(urls, progress_callback=progress)

CLI batch command:

# Scrape multiple videos concurrently
yt-network-scraper batch "URL1" "URL2" "URL3" --workers 4 --pretty --out batch.json

# Or read URLs from a file (one per line)
yt-network-scraper batch --file urls.txt --workers 3 --comments 50 --out batch.json

Sample Response

Here is an example of the actual JSON output you get when scraping a real video. This was produced by running:

yt-network-scraper video "https://youtu.be/ALyQ-c9_HBI" --comments 5 --pretty

The result is a VideoResult object. Calling result.to_dict() (or using --pretty / --out in the CLI) produces JSON with this structure:

{
  "video_id": "ALyQ-c9_HBI",
  "source_url": "https://www.youtube.com/watch?v=ALyQ-c9_HBI&hl=en&persist_hl=1",
  "metadata": {
    "video_url": "https://www.youtube.com/watch?v=ALyQ-c9_HBI",
    "title": "এটিএন বাংলার সন্ধ্যা ৭ টার সংবাদ । 16.08.2026 | Today News ...",
    "description": "#atn #atnbangla #atnbanglanews ... Fair Usage Policy: ...",
    "views": 64649,
    "channel_name": "ATN Bangla News",
    "channel_id": "UCbgcYEdMsuypG2NJ-znBp3w",
    "channel_url": "http://www.youtube.com/@ATNBanglanews",
    "channel_subscribers": "10.3M subscribers",
    "upload_date": "2026-08-16T07:45:34-07:00",
    "publish_date": "2026-08-16T07:45:34-07:00",
    "duration_seconds": 2028,
    "category": "News & Politics",
    "is_live": false,
    "keywords": ["atn bangla news", "atnbangla", "bangla news", ...],
    "thumbnail": "https://i.ytimg.com/vi/ALyQ-c9_HBI/maxresdefault.jpg"
  },
  "engagement": {
    "comment_count_scraped": 5,
    "likes": 652,
    "views": 64649,
    "dislikes": {
      "source": "returnyoutubedislikeapi.com",
      "dislikes": 10,
      "likes": 648,
      "rating": 4.94,
      "view_count": 63292
    },
    "comment_count": 8
  },
  "transcript": {
    "available": true,
    "segments": [
      {
        "text": "আসসালামু আলাইকুম। এটিএন বাংলা সংবাদে সবাইকে স্বাগত ...",
        "start_ms": 8000,
        "duration_ms": null,
        "time": "0:08"
      },
      {
        "text": "রাজপথে বিশৃঙ্খলা সৃষ্টিকারীদের রুখে দেওয়ার আহ্বান ...",
        "start_ms": 16000,
        "time": "0:16"
      }
    ],
    "text": "আসসালামু আলাইকুম। এটিএন বাংলা সংবাদে সবাইকে স্বাগত ...",
    "language": "bn",
    "name": "Bangla (auto-generated)",
    "is_auto_generated": true,
    "source": "browser_network_get_panel"
  },
  "summary": {
    "available": true,
    "text": "আসসালামু আলাইকুম। এটিএন বাংলা সংবাদে সবাইকে স্বাগত ...",
    "method": "lead_sentences"
  },
  "comments": [
    {
      "comment_id": "Ugxxd7ztzYUkFIlchVl4AaABAg",
      "likes": 5,
      "reply_count": 0,
      "is_pinned": false,
      "is_hearted": true,
      "author": "@MdHoksap",
      "author_channel_id": "UC5DBcRWdv8oCmPhIauoy3gw",
      "author_channel_url": "/@MdHoksap",
      "text": "তারেক রহমান চাঁন্দাবাজের জন্য অপযোগী ...",
      "published": "3 hours ago"
    },
    {
      "comment_id": "UgzgNUoDsKC25nuDmhp4AaABAg",
      "likes": 0,
      "reply_count": 0,
      "is_pinned": false,
      "is_hearted": true,
      "author": "@MinhajUddin-s8n",
      "text": "SALARY OF ALL GOVT WORKERS IN SENIOR RANK MUST BE REDUCED ...",
      "published": "1 hour ago"
    }
  ],
  "network": {
    "access_status": {
      "blocked": false,
      "reasons": [],
      "message": "Access looks normal"
    },
    "api_key_found": true,
    "captured_event_count": 2256,
    "dom_scraping": false,
    "bot_evasion": false
  }
}

What each field contains

API Usage

YouTubeScraper

The main scraper class. Must be used as a context manager to manage the browser lifecycle.

from yt_network_scraper import YouTubeScraper, ScraperConfig

config = ScraperConfig(
    headless=True,           # Run Chrome in headless mode
    timeout=25,              # Browser page-load timeout (seconds)
    max_comments=25,         # Maximum comments to fetch
    transcript_language="en", # Preferred transcript language
    request_delay=1.5,       # Delay between fallback requests (seconds)
    max_page_retries=2,      # Retries on access-block pages
)

with YouTubeScraper(config) as scraper:
    result = scraper.get_video("VIDEO_ID")

VideoResult

The return type of get_video(). Contains:

Show Chrome (for debugging)

yt-network-scraper video VIDEO_ID --no-headless

Custom timeout and retries

yt-network-scraper video VIDEO_ID --timeout 60 --retries 5

## Configuration

All configuration is done through the `ScraperConfig` dataclass:

| Parameter | Default | Description |
|-----------|---------|-------------|
| `headless` | `True` | Run Chrome in headless mode |
| `timeout` | `25` | Browser page-load timeout in seconds |
| `max_comments` | `25` | Maximum number of comments to fetch |
| `transcript_language` | `"en"` | Preferred ISO language code for transcripts |
| `request_delay` | `1.5` | Base delay between fallback network requests (seconds) |
| `max_page_retries` | `2` | Number of retries when YouTube returns a block page |
| `user_agent` | Chrome 125 UA | User-Agent string for the browser and HTTP session |

## Package Architecture

The package is organized into focused, single-responsibility modules under `src/yt_network_scraper/`. This separation of concerns makes the codebase easy to test, maintain, and extend:

<img src="https://raw.githubusercontent.com/DIP-RO/youtube-scrapper/main/docs/images/module-graph.png" alt="Module dependency graph" width="680" />

| Module | Responsibility |
|--------|---------------|
| `__init__.py` | Public API exports — `YouTubeScraper`, `ScraperConfig`, all models, all exceptions |
| `client.py` | HTTP/network layer — Selenium browser lifecycle, Chrome DevTools log capture, innertube API calls, Return YouTube Dislike API integration |
| `scraper.py` | Orchestration layer — coordinates the full scrape workflow: load page → capture network → parse metadata → fetch transcript → fetch comments → fetch dislikes → generate summary → build `VideoResult` |
| `parsing.py` | Pure parsing functions — extracts metadata, transcript, comments, and access-block status from YouTube JSON payloads. No network calls. |
| `models.py` | Typed dataclass models — `VideoResult`, `VideoMetadata`, `Transcript`, `TranscriptSegment`, `Comment`, `Engagement`, `DislikeData`, `Summary`, `AccessStatus`, `NetworkInfo`. Each has `to_dict()` for JSON serialization. |
| `exceptions.py` | Exception hierarchy — `ScraperError` (base), `InvalidVideoURLError`, `AccessBlockedException`, `SeleniumNotInstalledError`, `BrowserNotInitializedError`, `TranscriptUnavailableError` |
| `utils.py` | Utilities — URL validation, video ID extraction, text summarization, sentence splitting, key lookup helpers, HTML unescaping |
| `cli.py` | Command-line interface — argparse-based CLI with `video` subcommand |

### How a scrape works

<img src="https://raw.githubusercontent.com/DIP-RO/youtube-scrapper/main/docs/images/scrape-flow.png" alt="Scrape flowchart" width="820" />

### Design principles

- **Network layer is isolated** — all Selenium and HTTP calls live in `client.py`. Parsing functions in `parsing.py` are pure and take dicts as input, making them trivial to test with fixtures.
- **Typed models everywhere** — the scraper never returns raw dicts. Every result is a typed dataclass with documented fields, optional fields are `None` when unavailable, and `to_dict()` produces clean JSON.
- **Defensive parsing** — YouTube changes payload shapes frequently. Every parser uses safe key lookups (`find_key`, `find_all_keys`) and returns `None` or empty lists for missing fields instead of raising exceptions.
- **No bot evasion** — the scraper detects access blocks but never tries to bypass them. If YouTube returns a CAPTCHA or consent wall, the `network.access_status` field reports it and the scrape completes with available data.
- **Configurable behavior** — `ScraperConfig` controls headless mode, timeout, retries, delays, comment limits, transcript language, and user agent. Sensible defaults work for most cases.

## Error Handling

The scraper uses a typed exception hierarchy. All exceptions inherit from `ScraperError`:

```python
from yt_network_scraper import YouTubeScraper, ScraperError

try:
    with YouTubeScraper() as scraper:
        result = scraper.get_video("bad_url")
except ScraperError as e:
    print(f"Scraping failed: {e}")

The scraper is designed to be defensive — YouTube frequently changes internal payload shapes. Missing fields are returned as None or empty lists rather than raising exceptions. The network.access_status field in the result indicates whether YouTube returned an access challenge.

Development

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=yt_network_scraper

# Build the package
python -m build

# Validate the build
twine check dist/*

Running Tests

pytest

The test suite uses mocked HTTP responses and mocked Selenium drivers — no live YouTube requests or browser instances are required. Tests cover:

• Package imports and public API surface
• Data model serialization
• URL parsing and ID extraction
• YouTube payload parsing (metadata, transcripts, comments)
• Network fetching with mocked sessions (success, errors, edge cases)
• Client orchestration with mocked Selenium
• CLI argument parsing and output

License

This project is licensed under the MIT License. See LICENSE for details.

Attribution

Developed and maintained by Dipro Paul.

• GitHub: github.com/DIP-RO
• LinkedIn: linkedin.com/in/dipro-paul

I release Python packages that make development easier — scraping, automation, data tools, and more. Follow for new releases.

Disclaimer

This package is intended for legitimate research, data analysis, and automation of publicly available YouTube video data. Users are responsible for complying with:

• YouTube's Terms of Service
• Applicable local and international laws
• Rate limiting and access restrictions

This package does not bypass CAPTCHAs, evade bot detection, circumvent authentication, or harvest credentials. If YouTube returns an access challenge, the scraper reports it rather than attempting to work around it. Use responsibly and at your own risk.