Open Notebook — Self-Hosted NotebookLM Alternative

Introduction

Open Notebook is an open-source alternative to Google NotebookLM that lets you upload documents, generate audio overviews, and chat with your sources using any LLM provider. It gives you full control over your data and model choices while offering the same core research workflow.

What Open Notebook Does

• Upload PDFs, web pages, and text to build a personal knowledge base
• Generate podcast-style audio summaries from your sources
• Chat with your documents using RAG-powered Q&A
• Connect to multiple LLM backends including OpenAI, Anthropic, and local models
• Organize research into notebooks with tagging and search

Architecture Overview

Open Notebook runs as a containerized web application with a TypeScript frontend and Python backend. It uses vector embeddings stored in a local database for retrieval-augmented generation, a task queue for async audio generation, and a pluggable LLM adapter layer that routes requests to your configured provider.

Self-Hosting & Configuration

• Deploy with Docker Compose on any Linux server or local machine
• Configure LLM provider API keys in the .env file
• Set up object storage for uploaded documents and generated audio
• Supports PostgreSQL with pgvector for embedding storage
• Optionally enable authentication for multi-user deployments

Key Features

• Provider-agnostic: swap between OpenAI, Anthropic, Ollama, or any OpenAI-compatible API
• Audio overview generation turns documents into listenable podcast episodes
• Local-first architecture keeps all data on your own infrastructure
• Notebook-based organization groups related sources and conversations
• Extensible plugin system for custom document processors

Comparison with Similar Tools

• Google NotebookLM — cloud-only, limited to Google's models, no self-hosting option
• AnythingLLM — broader scope but less focused on the research notebook workflow
• Khoj — AI second brain with search, but lacks audio overview generation
• RAGFlow — focuses on document parsing and RAG, less emphasis on notebook UX

FAQ

Q: Which LLM providers are supported? A: OpenAI, Anthropic, Google, Ollama, and any OpenAI-compatible endpoint. Configure via environment variables.

Q: Can I run it fully offline? A: Yes, pair it with a local model through Ollama and all processing stays on your machine.

Q: What file formats can I upload? A: PDF, DOCX, plain text, Markdown, and web URLs. The parser pipeline is extensible.

Q: How does audio generation work? A: It synthesizes a conversational summary of your sources using TTS, producing a podcast-style overview you can listen to.

Sources

• https://github.com/lfnovo/open-notebook
• https://open-notebook.ai