When an AI Gets the Keys to NotebookLM

What happens if you give an AI agent full programmatic access to Google's NotebookLM? No clicking. No UI. Just raw API-level control over your research workflows.

That's exactly what notebooklm-py does. It's a Python wrapper that turns NotebookLM into something you can script, automate, and hand off to an LLM agent.

What It Does

Notebooklm-py exposes NotebookLM's core actions as Python functions. You can:

• Create and manage notebooks
• Upload sources (PDFs, Google Docs, web links)
• Generate summaries, FAQs, study guides, and timelines
• Ask questions about your sources and get grounded answers
• List all notebooks, sources, and outputs

Under the hood, it reverse-engineers the internal API endpoints NotebookLM uses. This means you get the same capabilities you'd see in the browser, but accessible from code.

Why It's Cool

The real magic isn't the wrapper itself, it's what you can build on top of it. Here's why this stands out:

Agent-ready. You can plug this into any LLM tool-use framework (LangChain, AutoGPT, custom agents) and give it "read and synthesize this research paper" capabilities. The agent calls the notebook, uploads a PDF, generates a briefing, and returns the result.

Batch processing. Need to digest 50 PDFs and create study guides for each? That's a 10-line script now.

Context retention. Unlike feeding raw text to an LLM, NotebookLM keeps your source material. You can ask follow-up questions, get citations, and maintain context over long sessions.

No reverse-engineering hell. The library handles the auth tokens and payload formatting. You just write Python.

How to Try It

Install with pip:

pip install notebooklm-py

Then authenticate (you'll need a Google account with NotebookLM access):

from notebooklm import NotebookLM

client = NotebookLM()
client.auth()  # Follow the prompt to paste auth tokens

# Create a notebook
notebook = client.create_notebook("My Research")

# Add a source
source = notebook.add_source("https://arxiv.org/abs/2302.04761")

# Generate a summary
summary = notebook.generate("summary")
print(summary)

The README has a full example workflow including how to export the auth tokens for headless/automated use. For a live demo, the repo links to a Colab notebook you can run directly.

Final Thoughts

This is one of those projects that makes you go "oh, I can automate that too." If you're already deep into research workflows, content summarization, or building AI agents that need to handle long-form documents, this saves you the usual scraping-and-chunking headache.

Is it production-grade? It's a reverse-engineered client, so treat it like an experimental tool rather than a stable API. You might need to update auth tokens periodically. But for prototyping, personal projects, or internal tools, it's remarkably functional.

Sometimes the best libraries are the ones that just expose an existing product's hidden API. Notebooklm-py does exactly that, and it's already sparking ideas in the dev community about what an AI agent could do when given unfiltered access to research tools.

Found this on @githubprojects