Comprehensive Python API for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—from Python or the command line.
Source & Development: https://github.com/teng-lin/notebooklm-py
⚠️ Unofficial Library - Use at Your Own RiskThis 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.
🤖 AI Agent Tools - Integrate NotebookLM into Claude Code or other LLM agents. Ships with Claude Code skills for natural language automation (notebooklm skill install), or build your own integrations with the async Python API.
📚 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.
| Method | Best For |
|---|---|
| Python API | Application integration, async workflows, custom pipelines |
| CLI | Shell scripts, quick tasks, CI/CD automation |
| Agent Skills | Claude Code, LLM agents, natural language automation |
| Category | Capabilities |
|---|---|
| Notebooks | Create, list, rename, delete |
| Sources | URLs, YouTube, files (PDF, text, Markdown, Word, audio, video, images), Google Drive, pasted text; refresh, get guide/fulltext |
| Chat | Questions, conversation history, custom personas |
| Research | Web and Drive research agents (fast/deep modes) with auto-import |
| Sharing | Public/private links, user permissions (viewer/editor), view level control |
| Type | Options | Download Format |
|---|---|---|
| Audio Overview | 4 formats (deep-dive, brief, critique, debate), 3 lengths, 50+ languages | MP3/MP4 |
| Video Overview | 2 formats, 9 visual styles (classic, whiteboard, kawaii, anime, etc.) | MP4 |
| Slide Deck | Detailed or presenter format, adjustable length | |
| Infographic | 3 orientations, 3 detail levels | PNG |
| Quiz | Configurable quantity and difficulty | JSON, Markdown, HTML |
| Flashcards | Configurable quantity and difficulty | JSON, Markdown, HTML |
| Report | Briefing doc, study guide, blog post, or custom prompt | Markdown |
| Data Table | Custom structure via natural language | CSV |
| Mind Map | Interactive hierarchical visualization | JSON |
These features are available via API/CLI but not exposed in NotebookLM's web interface:
- Batch downloads - Download all artifacts of a type at once
- Quiz/Flashcard export - Get structured JSON, Markdown, or HTML (web UI only shows interactive view)
- Mind map data extraction - Export hierarchical JSON for visualization tools
- Data table CSV export - Download structured tables as spreadsheets
- Source fulltext access - Retrieve the indexed text content of any source
- Programmatic sharing - Manage permissions without the UI
# Basic installation
pip install notebooklm-py
# With browser login support (required for first-time setup)
pip install "notebooklm-py[browser]"
playwright install chromiumFor contributors or testing unreleased features:
pip install git+https://github.com/teng-lin/notebooklm-py@main
16-minute session compressed to 30 seconds
# 1. Authenticate (opens browser)
notebooklm login
# 2. Create a notebook and add sources
notebooklm create "My Research"
notebooklm use <notebook_id>
notebooklm source add "https://en.wikipedia.org/wiki/Artificial_intelligence"
notebooklm source add "./paper.pdf"
# 3. Chat with your sources
notebooklm ask "What are the key themes?"
# 4. Generate content
notebooklm generate audio "make it engaging" --wait
notebooklm generate video --style whiteboard --wait
notebooklm generate quiz --difficulty hard
notebooklm generate flashcards --quantity more
notebooklm generate slide-deck
notebooklm generate infographic --orientation portrait
notebooklm generate mind-map
notebooklm generate data-table "compare key concepts"
# 5. Download artifacts
notebooklm download audio ./podcast.mp3
notebooklm download video ./overview.mp4
notebooklm download quiz --format markdown ./quiz.md
notebooklm download flashcards --format json ./cards.json
notebooklm download slide-deck ./slides.pdf
notebooklm download mind-map ./mindmap.json
notebooklm download data-table ./data.csvimport asyncio
from notebooklm import NotebookLMClient
async def main():
async with await NotebookLMClient.from_storage() as client:
# Create notebook and add sources
nb = await client.notebooks.create("Research")
await client.sources.add_url(nb.id, "https://example.com", wait=True)
# Chat with your sources
result = await client.chat.ask(nb.id, "Summarize this")
print(result.answer)
# Generate content (podcast, video, quiz, etc.)
status = await client.artifacts.generate_audio(nb.id, instructions="make it fun")
await client.artifacts.wait_for_completion(nb.id, status.task_id)
await client.artifacts.download_audio(nb.id, "podcast.mp3")
# Generate quiz and download as JSON
status = await client.artifacts.generate_quiz(nb.id)
await client.artifacts.wait_for_completion(nb.id, status.task_id)
await client.artifacts.download_quiz(nb.id, "quiz.json", output_format="json")
# Generate mind map and export
result = await client.artifacts.generate_mind_map(nb.id)
await client.artifacts.download_mind_map(nb.id, "mindmap.json")
asyncio.run(main())# Install via CLI or ask Claude Code to do it
notebooklm skill install
# Then use natural language:
# "Create a podcast about quantum computing"
# "Download the quiz as markdown"
# "/notebooklm generate video"- CLI Reference - Complete command documentation
- Python API - Full API reference
- Configuration - Storage and settings
- Troubleshooting - Common issues and solutions
- API Stability - Versioning policy and stability guarantees
- Development Guide - Architecture, testing, and releasing
- RPC Development - Protocol capture and debugging
- RPC Reference - Payload structures
- Changelog - Version history and release notes
- Security - Security policy and credential handling
| Platform | Status | Notes |
|---|---|---|
| macOS | ✅ Tested | Primary development platform |
| Linux | ✅ Tested | Fully supported |
| Windows | ✅ Tested | Tested in CI |
MIT License. See LICENSE for details.
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