teng-lin / notebooklm-py

# notebooklm-py
**A Comprehensive Google Gemini Notebook Skill & Unofficial Python API.** Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.
> **Note (July 2026):** Google rebranded **NotebookLM** to **Gemini Notebook**. It remains the same standalone product (now also reachable inside the Gemini app), existing links redirect automatically, and this library drives the same underlying service and works unchanged. The package keeps the `notebooklm-py` name.
**Source & Development**: 
> **⚠️ Unofficial Library - Use at Your Own Risk**
>
> This library uses **undocumented Google APIs** that can change without notice.
>
> - **Not affiliated with Google** - This is a community project
> - **APIs may break** - Google can change internal endpoints anytime
> - **Rate limits apply** - Heavy usage may be throttled
>
> Best for prototypes, research, and personal projects. See Troubleshooting for debugging tips.
## What You Can Build
🤖 **AI Agent Tools** - Integrate NotebookLM into Claude Code, Codex, and other LLM agents. Ships with a root NotebookLM skill for GitHub and `npx skills add` discovery, local `notebooklm skill install` support for Claude Code and `.agents` skill directories, and repo-level Codex guidance in `AGENTS.md`.
📚 **Research Automation** - Bulk-import sources (URLs, PDFs, YouTube, Google Drive), run web/Drive research queries with auto-import, and extract insights programmatically. Build repeatable research pipelines.
🎙️ **Content Generation** - Generate Audio Overviews (podcasts), videos, slide decks, quizzes, flashcards, infographics, data tables, mind maps, and study guides. Full control over formats, styles, and output.
📥 **Downloads & Export** - Download all generated artifacts locally (MP3, MP4, PDF, PNG, CSV, JSON, Markdown). Export to Google Docs/Sheets. **Features the web UI doesn't offer**: batch downloads, quiz/flashcard export in multiple formats, mind map JSON extraction.
## Use Cases & Recipes
NotebookLM is a **grounded** engine: Gemini does the heavy reading and answers from *your* sources with citations. The winning pattern is to let it do the expensive analysis while your agent (Claude Code, Codex, …) orchestrates and handles the final mile — using NotebookLM as a **zero-token synthesis + memory layer an agent drives in a loop**, and pulling structured artifacts **out** in bulk and in richer, scriptable formats. Recipes people build on top of this library, grouped by what they use NotebookLM *as*:
**Spend fewer tokens** — let NotebookLM do the expensive thinking:
- **🪙 Zero-token research offload** — Throw 30 documents into a notebook, let Gemini do the heavy analysis, and have your agent spend tokens only on the final polish. The agent just orchestrates (`create` → `source add` → `ask`); the reasoning happens server-side. *In the wild: a four-workflow guide to stop Claude Code burning tokens on NotebookLM