deeptutor vs RAGFlow
Two of the top projects in RAG and knowledge, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
deeptutor
Self-hosted AI tutor with knowledge bases, guided learning, books and quizzes
RAGFlow
RAG engine with deep document parsing, template chunking and agentic retrieval
Verdict
deeptutor ranks higher, 78 to 74. Of the 5 score parts we measure for both, deeptutor leads on 1, RAGFlow on 2 and 2 are even.
- AdoptionRAGFlow leads by 21 points, 95 to 74: a wide gap.
- FreshnessEven at 100.
- MaintenanceRAGFlow leads by 1 point, 93 to 92: close to even.
- Easy to rundeeptutor leads by 34 points, 67 to 33: a wide gap.
- Agent-readyEven at 30.
Pick deeptutor if you are
Students and educators self-hosting a document-grounded AI tutor
- Several retrieval engines selectable per knowledge base: LightRAG, PageIndex, GraphRAG, LlamaIndex
- Covers study workflows: guided learning, mastery gates, quizzes, question bank, daily practice
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
| What we compare | deeptutor | RAGFlow |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 74, popular | 95, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 93, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 30, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 41.4k | 92.2k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Go |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
deeptutor
DeepTutor is a Python backend with a Next.js 16 web UI for studying from your own documents. It combines chat over knowledge bases (LightRAG, PageIndex, GraphRAG, LlamaIndex, FAISS) with guided learning, generated books, quizzes, question banks and deep research. It also offers multi-user accounts, IM-connected Partners, MCP services and a CLI.
Who it is for: Students and educators self-hosting a document-grounded AI tutor
Strengths
- Several retrieval engines selectable per knowledge base: LightRAG, PageIndex, GraphRAG, LlamaIndex
- Covers study workflows: guided learning, mastery gates, quizzes, question bank, daily practice
- Multi-user deployment with per-user isolation, admin grants and learner/guardian accounts
- Wide provider and parser coverage, including Codex OAuth, MinerU, Tika and PyMuPDF4LLM
Weaknesses
- Fast release cadence with breaking refactors (v1.6.3) makes upgrades risky
- Large surface area: Partners, MCP, CLI apps and plugins add configuration overhead
- RAM, GPU and port requirements are not stated in the visible README
- Aimed at learning and study; less suited to general chat or enterprise search
- Docker + Compose
- Needs Python 3.11+, Node.js (Next.js 16), Docker (optional)
- Models: OpenAI-compatible providers, Anthropic, Gemini, OpenAI Codex OAuth, Lemonade (local)
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
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