🚨 Fork Status: This is a maintained fork of teng-lin/notebooklm-py containing critical fixes for:

• Source Addition: Fixed add_url_source and add_file_source parameters to match updated NotebookLM API (11-element array structure).
• Artifact Generation: Improved monitoring and status parsing for artifacts (Audio, Infographic, etc.).

Install this version:

pip install git+https://github.com/Tatine13/notebooklm-py.git@main

Unofficial Python library for automating Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—from Python or the command line.

Google NotebookLM is powerful, but:

1. No official API exists.
2. The web UI is manual and hard to automate.
3. Chatbots and agents can't easily interface with it.

notebooklm-py solves this by reverse-engineering the internal RPC protocol, giving you a robust Python client that behaves like a real user but running at code speed.

• Auth & Session Management: Handles cookies, headers, and token refresh automatically.
• Full Source Support: Add URLs (incl. YouTube), PDFs, text files, MP3s, and Google Drive docs.
• Chat Interface: Send queries and get referenced answers (citations included).
• Artifact Generation: Programmatically trigger Audio Overviews (podcasts), blog posts, FAQs, and study guides.
• Rich Responses: Parse complex RPC responses into clean Pydantic models.

pip install notebooklm-py

For Playwright-based login (recommended for initial auth):

pip install "notebooklm-py[browser]"
playwright install chromium

from notebooklm.auth import GoogleAuth

# Opens browser for login, saves credentials to local storage
await GoogleAuth().login()

import asyncio
from notebooklm import NotebookLMClient

async def main():
    async with NotebookLMClient() as client:
        # Create a new notebook
        notebook = await client.create_notebook("My AI Research")
        print(f"Created notebook: {notebook.title} ({notebook.id})")

        # Add a source (e.g., Wikipedia)
        await client.add_source(notebook.id, "https://en.wikipedia.org/wiki/Artificial_intelligence")

        # Ask a question
        chat_response = await client.query(notebook.id, "Summarize the history of AI")
        print(chat_response.answer)

if __name__ == "__main__":
    asyncio.run(main())

See examples/ for more advanced usage patterns.

Currently under active development. APIs may change.

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

# Run tests
pytest

MIT