Open Notebook is the MIT-licensed self-hosted alternative to Google's NotebookLM. With 25,200+ GitHub stars and 2,900+ forks at the v1.9.0 release on June 2, 2026, it has become the reference open-source implementation of the NotebookLM concept: a research environment that lets users organize multi-modal content, ask grounded questions against it, generate podcasts from it, and search across it, while keeping all data under the user's control. ## What Open Notebook Actually Does The core workflow is straightforward. The user creates a notebook, adds sources (PDFs, web pages, YouTube videos, audio files, Office documents), and Open Notebook indexes everything into a hybrid full-text plus vector search backend. From there, the chat surface is grounded against the notebook's sources, every response cites the chunks it used, and the podcast generator can turn the notebook into a multi-speaker audio episode. This is the same surface NotebookLM ships, but the model layer is user-chosen instead of locked to a single provider. ## 18+ AI Providers, Including Local Models Provider flexibility is the headline differentiator. Open Notebook supports OpenAI, Anthropic, Google, Groq, and a long tail of others including Ollama and LM Studio for fully local inference. The user can configure a different provider for embeddings, chat, and podcast generation if they want, which makes it practical to run cheap embeddings locally while reserving a frontier model for harder chat queries. For privacy-sensitive use cases the full stack including embeddings can run on a local Ollama instance. ## Multi-Modal Source Ingestion The ingestion pipeline handles more source types than most note apps. PDFs, audio files, YouTube videos, Office documents, web pages, and raw text are all normalized into the same chunked, vectorized representation. Audio and video sources go through transcription before indexing, which means users can chat against a Zoom recording or a YouTube lecture the same way they would chat against a PDF. ## Professional Podcast Generation The podcast feature is more sophisticated than a single-voice TTS pass. Open Notebook supports multi-speaker generation with distinct voice profiles, which produces a conversational episode out of the notebook's source material. This is the feature that turned the original NotebookLM into a viral product, and Open Notebook reproduces it with user-controlled voice models. ## REST API and Self-Hosted Deployment A full REST API exposes notebooks, sources, chat, and podcast generation programmatically, which is the path most teams adopt for embedding Open Notebook into existing knowledge systems. Self-hosted deployment is the default, with optional password protection for public-facing instances. The tech stack (Python FastAPI backend, Next.js and React frontend, SurrealDB storage, LangChain for orchestration) is conventional enough that operations teams can debug it without learning a new paradigm. ## Multi-Language UI The v1.9.0 release ships UI translations for 8+ languages. For an open-source research tool this is unusual investment, and it reflects how much of the user base is outside English-speaking markets where NotebookLM availability or data residency concerns drive adoption of a self-hosted alternative. ## Limitations The project's surface area is large, which means deployment is not a single docker run command. A production install needs SurrealDB configured, embedding and chat providers wired up, and ideally TLS in front of the web UI. Podcast generation quality is gated by the underlying TTS provider, so users running purely local stacks should expect a quality gap versus cloud TTS. The chat layer is grounded against the notebook but still inherits the hallucination risk of whichever chat model is configured, so citation review is not optional for high-stakes use. Finally, large notebooks with many video sources can produce substantial storage and indexing time on first ingestion, which surprises users coming from cloud notebooks where that cost is hidden. Within those caveats, Open Notebook is the cleanest self-hosted realization of the NotebookLM idea available in 2026, and the only one that lets users keep both their data and their model choice in their own hands.