Unofficial project — good to know before you start

This is not affiliated with Google. It talks to the same batchexecute endpoints the NotebookLM web app uses, with a browser fallback when they move. They are undocumented, so they can change without notice — when that happens we ship a fix, as we have for every change so far.

Two practical notes: use a dedicated Google account for automation, and expect NotebookLM's own quotas to apply at high volume. See Disclaimer for the full text.

What You Can Build

🔗 No-code automation pipelines — The 33-endpoint REST API means NotebookLM becomes a step in n8n, Zapier, Make, or a plain curl in cron. No agent, no MCP client, no Node in your stack — just HTTP. This is the half most NotebookLM libraries don't have.

🤖 Agent tooling — The same engine over MCP for Claude Code, Cursor and Codex, with a bundled skill that primes the agent on citation formats, the daily-quota-aware batch pattern, and transport selection.

📚 Research at volume — Multi-account rotation with automatic re-authentication, built for overnight runs of 1 000+ questions across several notebooks without babysitting.

🎙️ Full Studio generation — Audio overviews, video, infographics, reports, presentations, data tables, plus flashcards, quizzes and mind maps — generated and downloaded programmatically.

Use Cases & Recipes

NotebookLM is a grounded engine: Gemini reads your sources and answers from them, with citations. The winning pattern is to let it do the expensive reading while your own stack handles orchestration and the last mile.

Spend fewer tokens — offload the reading

• 🪙 Zero-token synthesis layer — Drop 30 documents in a notebook, let Gemini do the heavy analysis, and spend your agent's context only on the final polish. The reasoning happens server-side; your agent just orchestrates (add_notebook → source_add → notebook_ask).
• 💾 Answer cache you can re-read offline — vault_batch writes every answer to disk as structured JSON against a published schema, so a batch run becomes a corpus you can grep, diff, re-index, or feed to a retrieval layer — without re-querying and re-spending quota.

Wire it into things that aren't agents

• ⚙️ NotebookLM as an n8n / Zapier / Make step — Because it speaks plain HTTP, a citation-backed answer becomes one node in a workflow: a form submission triggers a question, the cited answer lands in a sheet, a Slack message, or a database. No agent runtime involved.
• 📄 Document intake pipeline — Watch a folder or an inbox, push new PDFs and URLs in as sources, and ask a standing set of questions against them on every arrival.

Grounded answers with a paper trail

• 🔍 Citations with the actual source text — Answers come back with source names and the quoted excerpts they rest on, extracted from the citation panel — so a claim can be checked, not just attributed.
• 🎓 Literature review at thesis scale — Batch 100+ research questions across multiple notebooks, rotate accounts as daily quotas run out, and resume where it stopped. Built for, and tested on, exactly this.

Get artifacts back out

• 🔁 One source set, every format — Fan a single notebook out to a podcast, a video, a slide deck, a report, a quiz and a mind map, then download them all locally.

In the Wild

Real deployments, not hypotheticals.

• 📚 A doctoral literature review at batch scale — The project was built for, and is continuously tested on, overnight runs of 1 000+ research questions spread across several notebooks: multi-account rotation picks up when a daily quota runs out, every answer is written to disk with its citations, and an interrupted run resumes instead of starting over. The batch pattern in vault_batch exists because a thesis needed it.

• 🔌 Replacing a RAG engine with the REST API — musnymubarak/Calim_Doc swapped a Gemini-based retrieval engine for this project's HTTP API, running it as a Docker service (notebooklm:3000) behind a full client and worker layer. A good illustration of the REST half: no agent runtime, no MCP client — NotebookLM simply became a backend service their Python app calls.

Built something with it? Open an issue — this section is for other people's work.

Features

Q&A with Citations

• Ask questions to NotebookLM and get accurate, citation-backed answers
• Source citation extraction with 5 formats: none, inline, footnotes, json, expanded (97% excerpt success rate)
• Session management for multi-turn conversations with auto-reauth on session expiry

Content Generation

Generate multiple content types from your notebook sources:

Video Visual Styles: classroom, documentary, animated, corporate, cinematic, minimalist

Language of generated content: pass language to any generator — a BCP-47 code (es, ja, pt_BR, zh_Hans) or a name in English or in the language itself ("Spanish", "Español"). 81 languages are accepted, and an unrecognised one is refused rather than quietly swapped for another. Set a default with NOTEBOOKLM_CONTENT_LANGUAGE; it is deliberately independent of NOTEBOOKLM_UI_LOCALE, which only picks the interface language the browser fallback reads.