notebooklm-py: The Unofficial API That Supercharges Google NotebookLM

What is notebooklm-py?

NotebookLM is one of the most popular AI-powered research and note-taking tools from Google, but until recently, its powerful features were locked behind a web interface. According to the project documentation, notebooklm-py is an open-source Python library that provides comprehensive programmatic access to NotebookLM functionality.

The unofficial API, developed by community contributor teng-lin, has exploded in popularity with over 3,900 GitHub stars and 496 forks, as reported by the GitHub trending page. It allows developers to automate virtually every aspect of NotebookLM from Python code or the command line.

For those interested in AI automation tools, notebooklm-py represents an exciting development in the ecosystem. You can learn more about similar tools in our Technology category.

Features Beyond the Web UI

While NotebookLM offers an impressive web interface, notebooklm-py unlocks capabilities that are not available in the browser. As documented on the project GitHub page, these exclusive features include:

• Batch downloads: Download all artifacts of a type at once
• Quiz and flashcard export in multiple formats (JSON, Markdown, HTML)
• Mind map data extraction as hierarchical JSON
• Data table CSV export for spreadsheet applications
• Slide deck as editable PowerPoint files
• Programmatic sharing and permissions management

These features make notebooklm-py particularly valuable for researchers and content creators who need to process large amounts of material efficiently. The ability to export mind maps as JSON, for example, enables integration with visualization tools that the web interface simply cannot support.

What You Can Build

AI Agent Tools

Integrate NotebookLM into Claude Code or other LLM agents. The library ships with Claude Code skills for natural language automation, as noted in the project readme. Install with a single command and start controlling NotebookLM with plain English. This enables powerful workflows where you can describe what you want in natural language and let the AI handle the details.

The agent skills functionality is particularly noteworthy because it allows non-programmers to leverage NotebookLM's capabilities through conversational commands. This democratizes access to advanced AI research tools.

Research Automation

Bulk-import sources including URLs, PDFs, YouTube videos, and Google Drive files. Run web and Drive research queries with auto-import and extract insights programmatically. Build repeatable research pipelines that would be impossible through the web UI alone. This is especially valuable for academics and researchers managing large volumes of sources.

The research automation features allow users to create sophisticated workflows that can process dozens of sources automatically. Students working on thesis papers, for instance, can set up automated pipelines that import, summarize, and cross-reference source materials.

Content Generation

Generate Audio Overviews (podcasts) in four formats, three lengths, and over 50 languages. Create video overviews with nine visual styles including classic, whiteboard, kawaii, and anime. Produce slide decks, infographics, quizzes, flashcards, data tables, mind maps, and study guides.

The variety of output formats makes this tool incredibly versatile for content creators. Educators can generate quizzes and flashcards for student review, while marketers can create podcast-style audio overviews of their content.

The official GitHub repository provides detailed documentation on all available features: https://github.com/teng-lin/notebooklm-py

Three Ways to Use notebooklm-py

Python API

For application integration, async workflows, and custom pipelines, use the Python API. It provides full access to all NotebookLM features with both synchronous and asynchronous options. The API is well-documented and includes comprehensive examples for common use cases.

Developers can import the client library and start creating notebooks, adding sources, and generating content within minutes. The async support makes it particularly suitable for high-volume applications.

CLI

For shell scripts, quick tasks, and CI/CD automation, the command-line interface is ideal. Run commands like notebooklm login, notebooklm create, and notebooklm generate audio directly from your terminal. This makes it easy to integrate into existing workflows.

The CLI is perfect for one-off tasks and automation scripts. Developers have reported using it to generate weekly content roundups automatically.

Agent Skills

For Claude Code, LLM agents, and natural language automation, install the skill and use commands like Create a podcast about quantum computing or Download the quiz as markdown. This brings the power of NotebookLM to AI assistants.

Installation and Requirements

Getting started is straightforward. Install with pip install notebooklm-py. For browser login support, use pip install notebooklm-py with the browser extra, followed by playwright install chromium. The library supports macOS, Linux, and Windows, with macOS as the primary development platform.

The project requires Python and has minimal dependencies beyond the standard library and authentication components. Detailed installation instructions are available on the project GitHub page.

You can find the package on PyPI at https://pypi.org/project/notebooklm-py/

Important Considerations

As noted in the project documentation, notebooklm-py is an unofficial library using undocumented Google APIs. This means APIs can change without notice, the project is not affiliated with Google, and rate limits apply. It is best suited for prototypes, research, and personal projects rather than production systems requiring guaranteed uptime.

Users should be aware that because the library relies on internal APIs, Google could change these at any time, potentially breaking existing integrations. The maintainers recommend against using it for critical production workflows.

The Future of NotebookLM Automation

With notebooklm-py, developers can now harness the full power of Google NotebookLM in ways the web interface never intended. Whether building AI agents, automating research workflows, or creating content at scale, this library opens up new possibilities that were previously inaccessible.

The project is actively maintained with regular updates and a growing community. According to the GitHub release page, the project has had multiple version updates in recent months, indicating continued development. For anyone serious about leveraging NotebookLM professionally, notebooklm-py has become an essential tool in their arsenal.