open-notebook
An Open Source implementation of Notebook LM with more flexibility and features
Star Growth
Overview
Open Notebook is an open-source, privacy-focused alternative to Google's Notebook LM that enables users to organize, analyze, and interact with multi-modal research content entirely on their local systems. The platform supports 16+ AI providers including OpenAI, Anthropic, Ollama, and LM Studio, giving users flexibility in model selection without vendor lock-in. It handles diverse content types including PDFs, videos, audio files, and web pages, providing both full-text and vector search capabilities across all materials. A standout feature is its advanced multi-speaker podcast generation system that can create professional audio content from research materials. The tool combines AI-powered chat functionality with comprehensive content organization, allowing users to have contextual conversations based on their uploaded research. With support for multiple UI languages and complete local operation, Open Notebook addresses growing privacy concerns while maintaining the sophisticated AI capabilities found in cloud-based alternatives. The platform's 21,000+ GitHub stars reflect strong community adoption and active development.
Deep Analysis
Self-hosted NotebookLM alternative with 16+ provider support and 4-speaker podcast generation — vs Google NotebookLM which is cloud-only with 2 speakers
⚡ Capabilities
- • Privacy-focused alternative to Google NotebookLM
- • Multi-model support with 16+ AI providers including local Ollama
- • Multi-modal content organization (PDFs, videos, audio, web pages)
- • Multi-speaker podcast generation (1-4 speakers with custom profiles)
- • Full-text and vector search across all content
- • AI chat powered by your research context
- • REST API for automation
- • Multi-language UI (English, Portuguese, Chinese, Japanese, Russian, Bengali)
🔗 Integrations
✓ Best For
- ✓ Privacy-conscious researchers who want NotebookLM-like features
- ✓ Users who want multi-provider AI with local model support
✗ Not Ideal For
- ✗ Teams wanting a fully managed cloud notebook service
- ✗ Users needing comprehensive citation management
Languages
Deployment
Pricing Detail
⚠ Known Limitations
- ⚠ Requires Docker for deployment
- ⚠ Citation support is basic compared to Google NotebookLM
- ⚠ UI is Streamlit-based — less polished than commercial alternatives
Pros
- + Complete data privacy with 100% local operation and no cloud dependency
- + Extensive AI provider support (16+ models) including local options like Ollama and LM Studio
- + Advanced multi-speaker podcast generation capability for professional audio content creation
Cons
- - Requires local hardware resources to run AI models and process content
- - Setup complexity may be higher compared to cloud-based alternatives
- - Performance dependent on local system specifications and chosen AI models
Use Cases
- • Academic researchers organizing papers, videos, and notes while maintaining complete data privacy
- • Content creators generating podcasts from research materials using multi-speaker AI voices
- • Enterprise teams analyzing confidential documents without sending data to external AI services