What is notebooklm-py?
notebooklm-py is a community-maintained Python library, command-line tool and agent skill for Google Gemini Notebook, formerly NotebookLM. It provides programmatic notebook and source management, source-grounded questions and answers, research workflows, content generation and bulk exports. Agent integrations include Claude Code and Codex, with MCP and REST servers available for additional automation.
The package runs locally or in headless environments on macOS, Linux and Windows. Its remote MCP connector can be self-hosted, but analysis and notebook storage remain on Google's infrastructure. Authentication requires Google account credentials. The project is not affiliated with Google and relies on undocumented interfaces that may change without notice; account quotas and rate limits still apply.
notebooklm-py screenshots
Explore the interface. Open a screenshot to view it at full size.
Source: official notebooklm-py project page.
Key capabilities
- Notebook management Create, copy, organize and delete notebooks, sources and notes, and manage sharing permissions.
- Source ingestion Import web pages, files, YouTube content, Google Drive documents and pasted text, with research workflows that can automatically import results.
- Grounded queries Ask questions against notebook sources, retrieve cited answers and save responses as notes.
- Content generation Produce audio and video overviews, slide decks, reports, quizzes, flashcards, mind maps and data tables.
- Artifact exports Download generated content in scriptable formats, including bulk exports and structured data extraction.
- Automation interfaces Access workflows through Python, the CLI, agent skills, an MCP server or a REST server.
Where notebooklm-py fits
The package fits research pipelines, internal documentation queries, study-material generation and content repurposing. Developers can embed it in Python applications or scripts, while AI agents can use notebooks as a source-grounded reference and retrieve generated artifacts for further processing. Scheduling and agent-side orchestration remain part of the surrounding setup rather than the package itself.
Before deployment, teams should verify authentication renewal, credential storage, account permissions and live quota availability. Browser session files grant account access, while the optional durable master token carries broader account risk and is documented for dedicated accounts. Workflows should tolerate throttling and upstream interface changes. The installation guide also identifies gaps in production integration guidance, including multi-tenant credential rotation and concurrency patterns.
Install
uv tool install "notebooklm-py[browser]"Reviews of notebooklm-py
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