RAGFlow vs SurfSense
Two of the top projects in RAG, knowledge and search, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
RAGFlow
RAG engine with deep document parsing, template chunking and agentic retrieval
SurfSense
Desktop NotebookLM-style agent that researches and edits local documents
Verdict
RAGFlow ranks higher, 90 to 87. Of the 4 score parts we measure for both, RAGFlow leads on 1, SurfSense on 2 and 1 is even.
- AdoptionRAGFlow leads by 31 points, 99 to 68: a wide gap.
- FreshnessEven at 100.
- MaintenanceSurfSense leads by 2 points, 94 to 92: close to even.
- Agent-readySurfSense leads by 55 points, 85 to 30: a wide gap.
Pick RAGFlow if you are
Teams self-hosting document Q&A over messy PDFs and Office files
- Template-based chunking with a visual view of chunks so humans can correct them
- Answers include traceable citations back to source chunks
Pick SurfSense if you are
Users who need cited Q&A and edits over private documents, offline
- Index stays on your disk; no account required
- Edits PDF, Word and Excel files as copies, never altering originals
| What we compare | RAGFlow | SurfSense |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 68, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 94, healthy |
| Agent-ready | 30, minimal | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 94.1k | 16.9k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2026 |
| Language | Go | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Mentioned |
RAGFlow
RAGFlow parses complex documents (PDF, Word, Excel, PowerPoint, scans, web pages) with its DeepDoc layout, OCR and table recognition, then chunks them by template and answers with traceable citations. It adds multi-step agentic retrieval with four thinking modes, plus knowledge compilation into wikis, graphs and mind maps. Services are written in Go and deployed with Docker Compose, with a web UI and HTTP API.
Who it is for: Teams self-hosting document Q&A over messy PDFs and Office files
Strengths
- Template-based chunking with a visual view of chunks so humans can correct them
- Answers include traceable citations back to source chunks
- Ingests many formats, plus sitemap crawling and Google BigQuery sync
- Configurable LLM, embedding and reranker models; multiple recall with fused re-ranking
Weaknesses
- Heavy stack: MySQL, MinIO, NATS, Kvrocks and a document engine; 16 GB RAM suggested
- macOS is not supported for the Docker deployment
- DeepDoc runs on CPU inference in the 1.0 release, which may slow parsing
- Current release is 1.0.0-rc1, a release candidate rather than a stable 1.0
- RAM ≥ 16 GB
- no GPU
- Docker + Compose
- Needs Docker, Docker Compose, MySQL, Elasticsearch or Infinity, MinIO, NATS, Kvrocks
- Models: Configurable LLM, embedding and reranker models via provider page
- port 80
SurfSense
SurfSense is a desktop app for Windows, macOS (Apple Silicon) and Linux that answers questions over your files with citations and can also create, edit and convert PDF, Word, Excel and PowerPoint documents. The index stays on disk and there is no account. You choose the model: any OpenAI-compatible API or a local one. Edits always produce a new copy.
Who it is for: Users who need cited Q&A and edits over private documents, offline
Strengths
- Index stays on your disk; no account required
- Edits PDF, Word and Excel files as copies, never altering originals
- Transcribes audio locally and reads scanned pages via OCR
- Works with any OpenAI-compatible API or a local model
Weaknesses
- Built-in plugins (Google Search, Web Crawl, etc.) need a SurfSense license; trial is 30 days
- Word and PowerPoint to PDF needs LibreOffice, not included in the installer
- Image generation needs a model you configure; none is chosen by default
- Hosted web app is being sunset; only the desktop app remains
- GPU optional
- Docker + Compose
- Needs LibreOffice (optional, for Office conversion)
- Models: OpenAI-compatible APIs, local models
- README: alternative to NotebookLM
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