Open Notebook vs RAGFlow
Two of the top rag and knowledge, side by side: score, setup, license, activity and what each review found.
Open Notebook
Self-hosted NotebookLM alternative with podcasts and 20+ model providers
RAGFlow
RAG engine with document parsing, agentic retrieval and citations
| What we compare | Open Notebook | RAGFlow |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 76, popular | 95, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 85, healthy | 93, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 40.1k | 92k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Not stated | Go |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
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
RAGFlow
RAGFlow parses documents (Word, slides, Excel, TXT, images, scans, web pages) with template-based chunking, then retrieves with multiple recall and fused re-ranking to produce answers with traceable citations. Recent releases add agentic multi-step retrieval with four thinking modes and Knowledge Compilation into wikis, graphs, trees and mind maps. Models are configured by name, address and API key for the LLM, embedding and reranker.
Who it is for: Teams building document Q&A with cited answers on their own servers
Strengths
- Chunk visualization lets you inspect and correct parsing before retrieval
- Citations link answers back to source chunks
- Ingests sitemaps and Google BigQuery with incremental sync
- Apache-2.0 with prebuilt Docker Compose deployment
Weaknesses
- Stack needs MySQL, MinIO, NATS, Kvrocks, ClickHouse and a document engine
- Go backend not supported on macOS; Linux x86_64 host required
- DeepDoc OCR and layout analysis run on CPU only in 1.0
- Current release is 1.0.0-rc1, a release candidate
- RAM ≥ 16 GB
- no GPU
- Docker + Compose
- Needs Elasticsearch or Infinity, MySQL, MinIO, NATS JetStream, Kvrocks, ClickHouse
- Models: configurable LLM, embedding, reranker
- port 80