LightRAG vs MaxKB
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
MaxKB
Enterprise knowledge-base agent platform with RAG, workflows and MCP tools
| What we compare | LightRAG | MaxKB |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 76, popular | 55, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 88, healthy | 96, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 40.1k | 23k |
| License | MIT (permissive) | GPL-3.0 (copyleft) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Sep 2026 |
| Language | Python | Python |
| 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
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
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