TubeStoevsky

Downloads a YouTube video transcript and turns it into literary prose through a LangChain LLM chain — outputs TXT + MD.

Works with any OpenAI-compatible chat completions API: a local gateway, Ollama, vLLM, OpenAI, or anything else speaking that protocol.

How it works

URL
 ├─ yt-dlp       subtitles (manual > auto)  -> {title}.srt
 ├─ SRT parser   timecodes and rolling dupes -> raw text
 ├─ LangChain    prose -> title -> Markdown
 └─ output       {title}.txt, {title}.md

Install

# as a CLI tool
uv tool install tubestoevsky   # or: pip install tubestoevsky
tubestoevsky --help

# from a checkout, for development
uv sync

Configuration

Copy .env.example to .env and set LLM_API_KEY. The file is read from the current directory, the project root, or ~/.config/tubestoevsky/.env.

Usage

tubestoevsky <URL>
tubestoevsky <URL> --lang en --out ./out
tubestoevsky <URL> --model gpt-4o-mini

# or without installing:
uvx tubestoevsky <URL>

# from a checkout:
uv run python -m transcript_app <URL>

Layout

transcript_app/
  __main__.py    CLI
  config.py      .env + CLI flags
  downloader.py  yt-dlp
  srt.py         subtitle parser
  chain.py       LangChain chain over the API
  prompts.py     step prompts
  outputs.py     .txt/.md writers

Notes

• LLM steps stream: some gateways fail to return monolithic responses.
• Accept-Encoding: identity and use_responses_api=False work around proxy hangs.
• Run long requests in the foreground.