LightRAG vs Open Notebook
Two of the top rag and knowledge, side by side: score, setup, license, activity and what each review found.
LightRAG
Graph-plus-vector RAG server with web UI and Ollama-compatible API
Open Notebook
Self-hosted NotebookLM alternative with podcasts and 20+ model providers
| What we compare | LightRAG | Open Notebook |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 80, widely used | 76, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 89, healthy | 85, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 40.1k | 40.1k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Sep 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
LightRAG
LightRAG indexes documents into a knowledge graph plus vector store and queries both layers, as a lighter alternative to Microsoft GraphRAG. The server package ships a REST API, a web UI for inserting and visualizing the graph, and Ollama-compatible /api routes for chat frontends. Parsing runs via MinerU, Docling or a native engine; production storage goes to PostgreSQL, Neo4j, MongoDB, Milvus or OpenSearch.
Who it is for: developers wanting graph-based RAG with a ready server
Strengths
- Dual-level graph and vector retrieval with fewer LLM calls than community-report GraphRAG
- Incremental updates and document deletion with graph regeneration from the LLM cache
- Three parsing engines and four chunking strategies, including paragraph-semantic
- Separate LLM settings per role: extract, query, keywords and VLM
Weaknesses
- Default KV, vector and graph stores are in-memory with file persistence, not for production
- Server binds 0.0.0.0 with every endpoint public until auth is configured
- Ollama-compatible /api routes stay open even with auth unless WHITELIST_PATHS is set
- docx smart headings and SVG rendering need extra spaCy models and libcairo
- no GPU
- Docker + Compose
- Needs PostgreSQL (recommended for production), Neo4j (optional), MongoDB (optional), Milvus (optional), OpenSearch (optional)
- Models: LLM and embedding providers configured in .env, tested with open models such as Qwen3-30B-A3B
Open Notebook
Open Notebook collects PDFs, audio, video, web pages and Office files into notebooks and offers cited chat, full-text and vector search, notes and multi-speaker podcast generation. It runs as two containers (SurrealDB plus a FastAPI/Next.js app) and talks to OpenAI, Anthropic, Google, Mistral, Groq, Ollama, LM Studio or any OpenAI-compatible server. A REST API and MCP integration expose the same features.
Who it is for: individual researchers who want a private NotebookLM
Strengths
- 20+ providers, including Ollama and LM Studio for fully local runs
- Podcasts with 1 to 4 speakers and custom episode profiles
- REST API on port 5055 and an MCP server for Claude Desktop or VS Code
- Two-service Docker Compose; keys stored encrypted with OPEN_NOTEBOOK_ENCRYPTION_KEY
Weaknesses
- Single-user; multi-user support is only a future direction in VISION.md
- No password by default and ports 8502/5055 bind to all interfaces
- Anthropic and Groq offer no embeddings, so a second provider is needed
- UI in 14 languages but provider setup is manual per model type
- no GPU
- Docker + Compose
- Needs SurrealDB
- Models: OpenAI, Anthropic, Google, Mistral, Groq, DeepSeek, xAI, OpenRouter, Cohere, Ollama, LM Studio, oMLX and any OpenAI-compatible endpoint
- port 8502
- README: alternative to NotebookLM