tubefetch 0.9.0

YouTube video metadata, transcript, and media fetcher

tubefetch

A Python CLI and library that fetches and extracts structured metadata and transcripts from YouTube videos, producing LLM-ready plain text, content hashes for change detection, and unified video bundles with batch processing, caching, and retry logic.

TubeFetch is a Python tool that extracts structured, AI-ready content from YouTube videos. Given one or more video IDs, URLs, playlists, or channels, it produces normalized metadata, transcripts, and optional media in formats optimized for downstream AI/LLM pipelines (summarization, fact-checking, RAG, search indexing, etc.). It provides content hashes for change detection, optional token count estimates, and unified video bundles. The tool supports both CLI and library usage with batch processing, intelligent caching, configurable retries via gentlify, and rate limiting.

Features

• Metadata — title, channel, duration, tags, upload date via yt-dlp (or YouTube Data API v3)
• Transcripts — fetched via youtube-transcript-api with language preference and fallback
• Media — optional video/audio download via yt-dlp
• Export formats — JSON, plain text, WebVTT (.vtt), SubRip (.srt)
• Batch processing — concurrent workers with per-video error isolation
• Caching — skip already-fetched data; selective --force overrides
• Retry — powered by gentlify with exponential backoff and jitter on transient errors
• Rate limiting — token bucket algorithm, shared across workers
• CLI + Library — use from the command line or import as a Python package

Installation

Requires Python 3.14+.

pip install tubefetch

For YouTube Data API v3 support (optional):

pip install tubefetch[youtube-api]

Note: The CLI accepts video IDs/URLs as positional arguments. Use tubefetch VIDEO_ID for the default behavior (metadata + transcript), or specialized commands like metadata, transcript, media for specific content.

Quick Start

CLI

# Fetch a single video
tubefetch dQw4w9WgXcQ

# Multiple videos
tubefetch VIDEO_ID_1 VIDEO_ID_2 VIDEO_ID_3

# From a file
tubefetch --file video_ids.txt

# With media download
tubefetch VIDEO_ID --download video

# Batch from a file
tubefetch --file video_ids.txt --workers 3

# Transcript only
tubefetch transcript dQw4w9WgXcQ --languages en,fr

# Metadata only
tubefetch metadata dQw4w9WgXcQ

# Media only (downloads video+audio by default)
tubefetch media dQw4w9WgXcQ

Specialized Commands

For exceptional cases when you only need specific data:

# Metadata only
tubefetch metadata VIDEO_ID

# Transcript only
tubefetch transcript VIDEO_ID

# Media only
tubefetch media VIDEO_ID

Library API

from tubefetch import fetch_video, fetch_batch, FetchOptions

# Single video
result = fetch_video("dQw4w9WgXcQ")
print(result.metadata.title)
print(result.transcript.segments[0].text)

# With options
opts = FetchOptions(out="./output", languages=["en", "fr"], download="audio")
result = fetch_video("dQw4w9WgXcQ", opts)

# Batch
results = fetch_batch(["dQw4w9WgXcQ", "abc12345678"], opts)
print(f"{results.succeeded}/{results.total} succeeded")

Output Structure

out/
├── <video_id>/
│   ├── metadata.json
│   ├── transcript.json
│   ├── transcript.txt
│   ├── transcript.vtt
│   ├── transcript.srt
│   └── media/
│       ├── video.mp4
│       └── audio.m4a
└── summary.json

Configuration

Options are resolved in this order (first wins):

1. CLI flags
2. Environment variables (prefix TUBEFETCH_)
3. YAML config file (tubefetch.yaml)
4. Defaults

CLI Flags

Flag	Description	Default
--id	Video ID or URL (repeatable)	—
--file	Text/CSV file with IDs	—
--jsonl	JSONL file with IDs	—
--id-field	Field name in CSV/JSONL	id
--out	Output directory	./out
--languages	Comma-separated language codes	en
--allow-generated	Allow auto-generated transcripts	true
--allow-any-language	Fall back to any language	false
--download	none, video, audio, both	none
--max-height	Max video height (e.g. 720)	—
--format	Video format	best
--audio-format	Audio format	best
--force	Force re-fetch everything	false
--force-metadata	Force re-fetch metadata only	false
--force-transcript	Force re-fetch transcript only	false
--force-media	Force re-download media only	false
--retries	Max retries per request	3
--rate-limit	Requests per second	2.0
--workers	Parallel workers for batch	3
--fail-fast	Stop on first failure	false
--strict	Exit code 2 on partial failure	false
--verbose	Verbose output	false

Environment Variables

All options can be set via environment variables with the TUBEFETCH_ prefix:

export TUBEFETCH_OUT=./output
export TUBEFETCH_LANGUAGES=en,fr
export TUBEFETCH_DOWNLOAD=video
export TUBEFETCH_YT_API_KEY=your-api-key

YAML Config File

Create tubefetch.yaml in the working directory:

out: ./output
languages:
  - en
  - fr
download: none
allow_generated: true
retries: 3
rate_limit: 2.0
workers: 3

Retry Configuration

tubefetch uses gentlify for intelligent retry management with exponential backoff and jitter.

How Retries Work

• Transient errors (rate limits, network errors, service errors) are automatically retried
• Permanent errors (video not found, transcripts disabled) fail immediately without retry
• Configurable attempts: Set --retries N to control max retry attempts (default: 3)
• Disable retries: Set --retries 0 for external retry management (e.g., with your own gentlify configuration)

Examples

from tubefetch import fetch_video, FetchOptions

# Default: 3 retry attempts
result = fetch_video("dQw4w9WgXcQ")

# Custom retry count
opts = FetchOptions(retries=5)
result = fetch_video("dQw4w9WgXcQ", opts)

# Disable internal retries (for external retry management)
opts = FetchOptions(retries=0)
result = fetch_video("dQw4w9WgXcQ", opts)

CLI:

# Custom retry count
tubefetch dQw4w9WgXcQ --retries 5

# Disable retries
tubefetch dQw4w9WgXcQ --retries 0

Exit Codes

Code	Meaning
0	Success (or partial failure without --strict)
1	Generic error (e.g. no IDs provided)
2	Partial failure with --strict
3	All videos failed

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run unit tests
python -m pytest tests/

# Run with coverage
python -m pytest tests/ --cov=tubefetch --cov-report=term-missing

# Run integration tests (requires network)
RUN_INTEGRATION=1 python -m pytest tests/integration/

License

MPL-2.0