Open-Source NotebookLM Complete Guide: 35K Stars — Drop in Docs, Q&A and Podcasts Automated
Want document Q&A and AI podcasts without sending data to Google? open-notebook (35K GitHub stars, MIT) is the self-hosted NotebookLM alternative — Docker in 2 minutes, 1–4 speakers, full REST API.
Want a conversational knowledge base from your documents — without sending that data to Google? This 35K-star open-source project lets you stand up your own NotebookLM in about two minutes.
NotebookLM is excellent. Nobody argues that. But one issue never goes away: every PDF and audio clip you upload lives on Google's servers.
Fine for public content. Project docs, client files, unpublished drafts? Are you really comfortable dumping all of that in?
That is when I found open-notebook — an open-source NotebookLM alternative with 35K GitHub stars and an MIT license.
1. See it first: drop in a PDF and ask
Using open-notebook feels almost identical to Google NotebookLM — except your data never left your own server.
Upload a PDF, ask “what does this section cover?” — answers stay grounded in the document, not invention.
Q&A is only step one. Hit podcast generation, pick two speakers, and it turns the source into a dialogue script, then synthesizes a two-person audio overview.
You can choose 1 to 4 speakers, each with a custom persona and voice — far more flexible than NotebookLM's two fixed hosts.
NotebookLM's two core jobs — document-grounded AI Q&A and automatic podcasts — are both handled well. PDFs, web pages, video, and audio all become searchable knowledge.
2. Two real advantages over NotebookLM
First, more flexible podcasts. NotebookLM locks you to two fixed AI hosts. open-notebook supports 1–4 speakers, each with custom roles and voices.
Second, a full REST API. This is the practical gap. open-notebook ships a complete REST API for automation — NotebookLM has no public equivalent.
3. The caveats you should hear upfront
Citations are still weak. NotebookLM can point to “page 3 of this PDF.” open-notebook currently offers only basic grounding.
It is not free. Bring your own AI API key (pay per use) or run Ollama locally (free, but hardware-heavy). NotebookLM at least has a free tier.
In short, it replaces the capability of “drop in docs and let AI read them for you” — not “zero-cost freeloading.”
4. Who should try it — and who should wait
Worth trying if: you know basic Docker and care about keeping data off the cloud; you want non-Google models (DeepSeek, Anthropic, local Ollama) for document Q&A; or you need an API to wire source-grounded chat into your own stack.
Skip for now if: you have never installed Docker — the deploy path is not short; you need page-level citations — not ready yet; you only want free AI — this is a self-hosted tool, not a free AI product.
Difficulty: medium · Docker Desktop + one AI API key (or Ollama for a fully free setup that burns VRAM).
5. Fastest start: Docker in about two minutes
Pull the docker-compose config first:
curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml
Set OPEN_NOTEBOOK_ENCRYPTION_KEY to any secret string, then start:
docker compose up -d
Wait 15–20 seconds, then open http://localhost:8502 in your browser.
Configure models: if you have an API key, pick a provider on the Models page — OpenAI, Anthropic, Google, DeepSeek, Mistral, and more (18 providers). For a fully free local run, use examples/docker-compose-ollama.yml from the repo — no API key required, but you need a GPU.
Just spin it up, drop in a PDF, and try Q&A. Twenty minutes is enough to decide whether to go deeper.
Closing
Most of what NotebookLM does, open-notebook can do too — with data staying fully in your hands.
If you care about data sovereignty, model choice, or API-driven workflows, a two-minute open-notebook deploy is worth it.
Project: github.com/lfnovo/open-notebook