If you're reading this, you probably love what NotebookLM is capable of doing, but perhaps don't feel comfortable handing it all your data or being in the Google ecosystem in general. While I personally have accepted the fact that a good portion of my digital life will exist on Google's servers, I know plenty of people who haven't and probably never will. It's a fair position to hold, and something I keep in mind when poking around for alternatives to the tools I already use.

So when I kept seeing the same self-hosted NotebookLM alternative come up in comparison threads, I figured it was worth a look - the tool is Open Notebook. Self-hosting is something I fall in and out of, and it depends more on the types of tools I'm using rather than a need for complete local control, and it's not even something I've really completely mastered. But this one had a reputation for being approachable, and the feature list had a couple of things that caught my attention beyond just the privacy wins.

Open Notebook in a nutshell

A mature NotebookLM alternative

Open Notebook is an open-source tool specifically built as an alternative to NotebookLM by a developer who goes by lfnovo on GitHub, and it runs in Docker on your machine. If you've used NotebookLM before the mental model is the same - you create a notebook, drop in your sources (PDFs, web URLs, YouTube links, raw text, audio, video), and from there you chat with the content and take notes. It's honestly the exact same thing, just implemented differently.

It uses RAG (retrieval-augmented generation) to ground its responses in your uploaded sources. So when you ask it something, it's pulling relevant chunks from your documents and feeding those to the model rather than making things up from training data. There's a wrinkle worth knowing about though. Open Notebook has two interaction modes - Chat and Ask. Chat gives you a continuous conversational back-and-forth, while Ask is designed for quick, targeted single-question lookups. So you can decide on a per-question basis whether the model gets the full document or just what's needed. And every source has three visibility levels you can flip between, going from completely private to fully available to the model. So even within one notebook you control exactly what the AI sees.

The obvious audience is anyone who doesn't want Google reading their research. But the more interesting case is people who want their own model in the driver's seat rather than whatever Gemini variant Google's pointing at NotebookLM at any given time.

Getting it running

It was mostly straightforward, until I tried to be clever

The only thing you actually install is Docker Desktop. Everything else lives in containers - Open Notebook itself, SurrealDB which is the database, and Ollama which is the local model runner. You grab the docker-compose.yml file from the official quick-start local guide on GitHub, drop it in an empty folder, and from a terminal in that folder you run docker compose up -d. That's the one command that does the heavy lifting. It pulls all three containers and wires them together. First run is a few gigs of downloads (Ollama is the biggest at around 4GB) so it's not instant, but it's only the once.

Once that finishes, you open http://localhost:8502 in your browser and the Open Notebook UI loads. The catch is that the app ships with zero models. It's just the interface, so you need both a chat model and an embedding model before you can do anything. And this is where I went off-script and tried to do something out of my scope…

I had the exact Gemma 4 E4B GGUF on disk already from my llama.cpp setup, and I figured I'd skip the redownload by importing it into Ollama through a Modelfile. I got it registered fine, but the first chat crashed instantly. I assumed it was perhaps an issue Ollama has with Gemma 4, or perhaps a memory issue from running on CPU. Either way, I then switched to a model that's known to behave well on this setup: Gemma 3 4B.

Open Notebook's docs literally use gemma3 as the example local model, it's well-supported in Ollama with proper caching, and it runs fine on modest hardware. One command pulls it (docker exec open_notebook-ollama-1 ollama pull gemma3:7b) and that's that. You also need an embedding model separately, which is what powers the source search and RAG. nomic-embed-text is the standard pick and it's a tiny download at around 270MB.

The last step is in Settings -> Models where I assigned gemma3:4b to Chat and Transformation, and nomic-embed-text to Embedding. The first message takes 30 to 60 seconds on CPU while the model loads into memory, but after that it stays loaded and responses come back quickly.

The stuff Google won't let you do

When Open Notebook actually opens things up

NotebookLM is Gemini-only. Whatever Google picks for it, you get, and there's no way to override the choice or send your sources to a server that isn't owned by Google. Open Notebook supports over 16 AI providers, which covers OpenAI, Anthropic, Mistral, DeepSeek, xAI, ElevenLabs for voice, OpenRouter as a meta-provider that routes to dozens more, and pretty much any OpenAI-compatible endpoint you want to point it at. And yes, Gemini's in there too if you still want it, just through your own API key.

And it's not just one model running everything. Each function gets its own assignment - Chat, Embedding, Transformation, Tools, and so on - so you can mix paid cloud APIs with local models and pick the right combo for the job. The official docs even suggest using a small local model like gemma3 for summarization and document queries while pairing that with something heavier like OpenAI or Claude for the actual chat.

The other thing NotebookLM keeps locked is the prompts themselves. Whatever logic Google's wired in for summaries and briefings is sealed off, so you can't see what the prompts say and you definitely can't change them. Open Notebook calls these Transformations and the prompts are fully editable - you can rework the defaults or build new ones for whatever workflow you're trying to create.

The privacy angle is the obvious headline, but the workflow customization is the deeper story of Open Notebook that I think gets undersold.

Once everything's running

The actual day-to-day with Open Notebook

The layout is three panels with Sources on the left, Notes in the middle, and Chat on the right. So if you're coming from NotebookLM then the shape will feel familiar.

Sources accept a wider range of inputs than NotebookLM does. You can drop in PDFs, paste in URLs, link YouTube videos (it grabs the transcript like NotebookLM does), or paste raw text directly, but there's also audio and video file support. Each source has to be processed before you can chat with it, and I will say this was probably the most frustrating part about it because it can take several minutes because Open Notebook extracts the text and runs it through nomic-embed-text to make it searchable. This is where your CPU will come to bite you.

The Notes panel works two ways. You can write manual notes yourself, or you can turn any LLM message into a Note with a save button on the chat response. Notes also carry the citations from the source that generated them which is pretty cool.

And of course, chat is where you actually do the work. You ask a question and you get back a grounded response with numbered references that link directly to the source chunk the claim came from. There's a Context indicator at the bottom that shows live what's being sent to the model in tokens and characters, so if a response is getting unwieldy you can dial individual sources up or down between visibility levels to tighten things.

There's also Podcasts, which I must admit I haven't tried yet. The idea is conceptually the same as NotebookLM's audio overviews, but you get more control over the configuration.

Right tool, wrong user

I think Open Notebook is a genuinely solid alternative. The privacy benefits are there and the workflow customization is plentiful compared to NotebookLM. But I'm not exactly the right audience for it. An hour of setup taught me where my self-hosting patience ends, and NotebookLM does the same work in seconds without hardware restrictions. So for anyone who actually wants this level of control, Open Notebook does earn its place.