RAGFlow vs MaxKB
Two of the top rag and knowledge, side by side: score, setup, license, activity and what each review found.
RAGFlow
RAG engine with document parsing, agentic retrieval and citations
MaxKB
Enterprise knowledge-base agent platform with RAG, workflows and MCP tools
| What we compare | RAGFlow | MaxKB |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 95, widely used | 55, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 96, healthy |
| Easy to run | 33, some setup | 50, easy |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 92.1k | 23k |
| License | Apache-2.0 (permissive) | GPL-3.0 (copyleft) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Sep 2026 |
| Language | Go | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Mentioned |
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
MaxKB
MaxKB runs as one Docker container (port 8080, state in one volume) with a RAG pipeline that uploads or crawls documents, a workflow engine with function library and MCP tool use, and zero-code embedding into other systems. It works with private models (DeepSeek, Llama, Qwen) and public APIs (OpenAI, Claude, Gemini, MiniMax) and handles text, image, audio and video. Built on Django, LangChain and PostgreSQL.
Who it is for: companies building internal Q&A bots and customer service agents
Strengths
- Single docker run with all data under one mounted volume
- Workflow engine with function library and MCP tool calling
- Crawls online documents into the knowledge base automatically
- Multimodal input and output: text, image, audio, video
Weaknesses
- GPL-3.0 limits bundling into proprietary products
- Ships with default admin password MaxKB@123..
- README gives no hardware guidance or provider configuration detail
- Detailed docs are on maxkb.cn, partly in Chinese
- no GPU
- Models: OpenAI, Claude, Gemini, MiniMax, DeepSeek, Llama, Qwen as private models
- port 8080
More in RAG and knowledge
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.