Open Notebook

Overview

Private, searchable AI notebook for multi-modal research and content.

Open Notebook is a self-hosted, privacy-focused open-source notebook for LLM workflows that preserves data sovereignty and supports local or cloud deployment. It supports 16+ AI providers (OpenAI, Anthropic, Ollama, LM Studio) for multi-model routing, provider choice, and cost control. Core features include multi-modal content management (PDFs, video, audio, web pages), full-text vector search, contextual chat, and citation-aware research exports. The platform provides multi-speaker podcast generation and customizable content transformation for media and documentation workflows. Developers get a REST API, external tool integration, Docker-based deployment options, and extensibility through source code. Typical users include researchers, data scientists, developers, and teams needing a private, searchable LLM notebook with multi-language UI.

Use Cases

• Create reproducible, citation-aware literature reviews with Open Notebook by ingesting PDFs, web sources, and datasets, using multi-model routing and full-text vector search to surface and annotate key passages, then export publication-ready reports with embedded citations.
• Build a self-hosted, privacy-first LLM workspace for sensitive research teams using Open Notebook's contextual chat, multi-modal content management, and developer APIs to prototype models, collaborate securely, and maintain full data control.
• Create multi-speaker research podcasts and searchable media artifacts from findings using Open Notebook's podcast generation, transcription, and contextual chat to auto-generate scripts, embed citations in exports, and publish audio alongside vector-searchable transcripts.

Who Is It For

• Software developers
• Content creators
• Podcast producers
• Data scientists
• Academic researchers