Open Notebook emerges as powerful open-source alternative to Google's NotebookLM

Open Notebook's AI-powered podcasts are a game-changer for productivity, provided you're willing to configure them right

Ayush Pande is a PC hardware and gaming writer. When he's not working on a new article, you can find him with his head stuck inside a PC or tinkering with a server operating system. Besides computing, his interests include spending hours in long RPGs, yelling at his friends in co-op games, and practicing guitar. When it comes to productivity-enhancing AI tools, it's hard to ignore NotebookLM. Capable of aggregating documents and academic material in neatly organized notebooks, Google's research app lets you run LLM queries on your own data banks instead of forcing the AI models to rely on their trained data. And by grounding the chat sessions in actual sources, NotebookLM also ensures you get precise responses with cited documents instead of hallucinated answers based on outdated information. But despite its advantages, I'm not a fan of NotebookLM's lack of support for anything besides Google's clankers, especially since I've already de-Googled my productivity stack. Fortunately, I came across Open Notebook a few months ago, and this FOSS tool can replicate nearly all the tools in NotebookLM. This includes the podcast generation facility, which can convert academic notes into audio overviews that I can listen to during mind-numbingly boring chores - and it's by far the most underrated aspect of Open Notebook. These 5 small tweaks made my self-hosted LLM setup way more productive Why workflow optimization matters more than massive hardware specs. Posts 2 By Yash Patel Open Notebook's podcast generation facility is better than its rival's It's pretty customizable, too Let me be clear: NotebookLM's podcast generation capabilities aren't terrible by any means. If anything, they can sound surprisingly realistic with their intonation and language flow compared to a barebones Open Notebook setup. However, the fact that you're limited to Google's first-party models isn't ideal if you're as averse to cloud models as I am. Unless you're willing to look into workarounds, NotebookLM only supports a maximum of two AI speakers for each podcast. Then there's the fact that NotebookLM has a daily hard cap of three audio overviews in the free version, regardless of whether you're creating new podcasts for different notebooks or regenerating the audio file due to factual inaccuracies in the sources. In contrast, Open Notebook can harness a bunch of LLM and TTS providers when creating podcasts, including both cloud platforms and local inference engines. Besides letting me freely choose the speakers for my podcasts, I can freely modify their personality, intonation, and backstory to match the energy of the source documents. Sure, Google's NotebookLM supports some customization options for audio overviews, but they're nothing compared to the custom episode and speaker profiles available on its FOSS counterpart. I ran Ollama and Open WebUI on a $200 mini PC and this local AI stack actually works Transforming a $200 mini PC into a versatile tool for everyday tasks and beyond. Posts 15 By Dhruv Bhutani Open Notebook also supports up to four AI speakers, which is pretty useful when I want distinct voices for long discussions spanning multiple sources. Assuming you've got a fully local pipeline for podcast generation as I do, you can create dozens of fully-voiced notes without worrying about paying a dime on premium subscriptions. While we're on this subject... It can even use a fully local pipeline for podcast generation Speaches + llama-server power my Open Notebook tasks Although Ollama is a decent option for Open Notebook, I prefer to use LLMs running on llama.cpp hosts for the inference tasks. Specifically, the Qwen3.6-35B-A3B running on my RTX 3080 Ti (with some experts offloaded to my CPU and RAM) serves as the outline and transcription model for the podcast generation operations. Meanwhile, the text-to-speech aspect is handled by a Speaches container running on the same machine that houses the Open Notebook instance. Speaches, in turn, uses Kokoro-82M-v1.0-ONNX for TTS operations when generating podcasts, though I've also configured it to run faster-whisper-small for the speech-to-text workloads required to process audio and video sources for my notebooks. Performance-wise, this setup is able to generate a 15ish minute podcast with three speakers in roughly 20 minutes, which is pretty impressive considering that everything runs on a local AI pipeline that doesn't connect to the cloud. But creating the right speaker and episode profiles is pretty important By default, Open Notebook includes a handful of speaker and episode configurations. But you'll have to manually modify them to fit your specific AI provider, or the podcast generation wizard will fail without throwing any errors (yes, I learned that the hard way). For the speaker profile, you'll have to choose the TTS model as well as the voices you wish to use for the podcast. It also includes the backstory and personality sections, where you can configure the role and energy of each speaker. Deals Save on Computers & Work Setup Deals for Local AI Rigs Discover savings on computers and work-setup essentials to power local AI workflows and podcast production. Explore discounts on desktop GPUs, CPUs, NVMe storage, RAM, cooling, audio interfaces, mics, and networking gear to build a reliable research rig. Deals Explore Computers & Work Setup Deals I tend to roll with two-speaker setups for simple podcasts, where one serves as the clear and succinct host while the other acts as the energetic and expressive expert on the topic. For complex notes, I typically add a third inquisitive voice that asks questions, with a fourth speaker responsible for summarizing important bits throughout the podcast. Meanwhile, the episode profiles set the number of segments, outline generation LLMs, and overall tone of the podcast. Since my notes revolve around home lab, DevOps projects, and coding documentation, I've set the briefing parameter to force the AI tools to provide detailed insights and maintain a no-nonsense approach throughout the audio overview. And that's just one of Open Notebook's many features As much as I adore the podcast generation facility, Open Notebook has plenty of tricks up its sleeve. The RAG-based chat is perfect for summarizing massive documents and answering queries with pinpoint accuracy, while the transformation operations are just as fantastic for analyzing notes. Toss a bulky LLM like Gemma-4-26B-A4B or Qwen3.6-35B-A3B, and Open Notebook becomes a productivity behemoth for research. Open Notebook See at Github Expand Collapse

I switched from NotebookLM to this open-source tool, and it does what Google won't

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. Want to stay in the loop with the latest in AI? The XDA AI Insider newsletter drops weekly with deep dives, tool recommendations, and hands-on coverage you won't find anywhere else on the site. Subscribe by modifying your newsletter preferences! 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. 4 reasons Open Notebook is the best self-hosted NotebookLM alternative No need to share your research data with Google anymore Posts 2 By Ayush Pande 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. NotebookLM is already great, but these 4 features would make it even better Good? Yes. Perfect? Not yet. Posts 5 By Mahnoor Faisal 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. Open Notebook See at Github See at Official Site Expand Collapse

I switched from NotebookLM to an open-source alternative -- the podcasts alone made it worth it

Amir is the Segment Lead for Software at MUO. He's a PharmD student who loves looking at numbers and spreadsheets. Inspired by his father's hobbies, Amir developed a knack for DIY projects and built his first quadcopter in high school. At 18, he began writing about 3D printing, and now contributes to MUO where he writes and edits productivity tools, AI and LLMs, spreadsheets, self-hosting, DIY projects, and a lot more. Amir also enjoys creating music, although its categorization as such remains open to interpretation. In addition to his academic pursuits, Amir is an avid gamer, car enthusiast, and proud owner of a 1993 Mitsubishi Galant. What's more interesting than NotebookLM is the concept behind it. A large language model rooted and grounded strictly on your material sounds really powerful, and it indeed is. NotebookLM started as an experiment, when a group of Google researchers decided to see what they could do with RAG. But once it grew in popularity and received more features as a result, it wasn't long until it became its own full Google product. NotebookLM deserves the praise it has received, but in the end, it is a proprietary closed-source tool. The concept of NotebookLM can go way beyond what NotebookLM offers right now, or ever will. No matter what your workflow is, your queries in NotebookLM will always use the cheapest Gemini model. And although Google has allowed a degree of customization recently, the Studio content, like the summaries and data tables, will always have a system prompt that's set by Google, not you. The Audio Overview podcasts will have the same two hosts, and always abide by the system prompt that Google has set. To this day, NotebookLM is a one-of-a-kind tool. No response has come from other big AI companies. But even if we had one, it wouldn't be any different -- it would just be limited to what Anthropic or OpenAI have baked into it. With all of that said, imagine my excitement when I came across the ultimate NotebookLM alternative. Not from Anthropic or OpenAI, but from a single maintainer, available freely on GitHub. Meet Open Notebook An open-source NotebookLM Open Notebook is a project by developer Luis Novo, available freely on GitHub. It does what NotebookLM does. You upload sources, chat with them, ask questions, generate summaries, and even make podcasts. Open Notebook is an interface. It has the code to do all of this, but it doesn't provide the AI models. With NotebookLM, you get Google's Gemini. With Open Notebook, you get nothing -- which is fantastic. Because that means you can hook up whatever model you want. This includes providers like OpenAI, Google, Anthropic, Groq, Mistral, DeepSeek, Azure, and -- to top it all off -- OpenRouter and OpenAI-compatible endpoints. Those last two are worth noting. With OpenRouter, you can access almost every LLM and pay as you go. One API key, and you have them all. I use this. But I also have Gemma 4 running on my own machine. That OpenAI-compatible endpoint here lets me plug local language models straight into Open Notebook. With a local model, you can set your own system prompt, adjust how the model is loaded, and control the temperature and other parameters. You can even train your own model and load it in. And with a local model, your notebook works offline, and none of your data ever leaves your machine. That said, I don't have a supercomputer. My local model is never going to match the big cloud models -- and I didn't want Open Notebook to be a compromise. I wanted it to be an upgrade. If you have a capable system, it is. But for me, I stuck with cloud models through APIs. What does that get me? The same setup as NotebookLM, except instead of a cheaper Gemini model answering my queries, I can see what Claude Opus 4.8 has to say about them. I can have a frontier model write the podcast transcript. The model for each aspect of the tool is configurable independently. You can assign a specific model to one transformation and a different one to another, depending on what fits best. If you're going to use it with a local model, you'll also need to host an embedding model on your machine. It's tiny, so don't worry about it. Fully customizable transformations The NotebookLM Studio equivalent Open Notebook's Transformations are roughly equivalent to the text features in NotebookLM's Studio. These are preset prompt actions you can apply to a source with a click -- dense summary, key insights, paper review, and so on. You can edit all of them, with no hidden system prompt, and you can create your own from scratch. You can also set a default Transformation that runs automatically on every source you upload. I have mine set to dense summary, so when I add something to a notebook, it gets embedded and immediately summarized without any extra steps. You can take this much further with custom Transformations. For instance, you could create one that looks for ideas connecting to a specific topic -- say, astrophysics -- then upload a batch of papers and have it run across all of them. It surfaces the relevant threads across every source automatically. It's really something. Fantastic podcasts They're better than NotebookLM NotebookLM took the world by storm when it introduced its podcast feature -- that's what made it mainstream. Podcasts are bigger than ever, because we always want to learn more without the effort of actually sitting down to read and study. And with NotebookLM, we have podcasts about exactly the topic we want. With Open Notebook, I can have podcasts that not only cover the topic I want, but cover it in exactly the way I want. The customization puts NotebookLM to shame. To generate a podcast, you'll need a speech model. You can host one yourself or connect one through API providers like OpenAI, OpenRouter, or ElevenLabs. Where NotebookLM has two fixed hosts, Open Notebook lets you add as many as you want. Each speaker gets a full profile -- their background, expertise, and viewpoints. From there, you can assemble panels. It comes with presets: the tech_review panel, for example, includes two technically-minded speakers who focus on the technical aspects of the material. I made a panel of two philosophers from different schools of thought and had them debate each other based on my journal entries. You can set the voice for each speaker, and you can use different speech providers for different speakers -- one from OpenAI, another from ElevenLabs, for instance. But the best part isn't the voices. It's that you decide what model writes the podcast script. You can go ambitious and use Opus 4.8 for it. And since you have full control over the prompt, you can shape the output exactly to your requirements. It's just a matter of how well you can prompt it. The ceiling is yours now Use it well Open Notebook takes the concept from NotebookLM and pushes it further than NotebookLM ever did. You get enough control that what you make of it is genuinely up to you. It's available via Docker, with a straightforward setup and minimal configuration required out of the box. It's hooked me for all the text and voice use cases I had from NotebookLM, and for me it's simply the better tool -- because of the control. That said, it won't accept image or video models, so it can't generate slide decks or summary videos. It also can't generate mind maps, though you could have it output the JSON for one and import that into a separate mind map viewer. Since you bring your own model and write your own prompts, the blade cuts both ways. The ceiling is high -- you can use frontier models from any provider, for both speech and text. But the floor is lower too. A poor prompt or a weaker model will produce results worse than NotebookLM's defaults. The tool is only as good as what you put into it.