LightRAG vs WeKnora
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
WeKnora
Enterprise knowledge base combining RAG Q&A, agents and generated wikis
| What we compare | LightRAG | WeKnora |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 80, widely used | 69, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 89, healthy | 89, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 40.1k | 33.1k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Sep 2026 | Oct 2026 |
| Last release | Sep 2026 | Sep 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
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
WeKnora
WeKnora turns team documents into knowledge bases with three modes: cited RAG answers, an agent that runs multi-step tasks with skills in Docker, E2B or Cube sandboxes, and auto-generated wiki pages with a knowledge graph. It syncs from Feishu, Confluence, GitLab, Notion and RSS, answers in WeCom, Slack and Telegram, and exposes an MCP server. Deploy with Docker Compose, Helm or one Lite binary on SQLite.
Who it is for: teams needing a self-hosted enterprise knowledge assistant
Strengths
- 29 built-in model vendors including OpenAI, DeepSeek, Qwen, Gemini, LiteLLM and Ollama
- Lite single binary with SQLite and in-memory queue for low-resource hosts
- Workspace RBAC with four roles, per-resource ownership and audit log
- Per-workspace MCP endpoints with own token, scope and rate limit
Weaknesses
- Many integrations target the Chinese ecosystem (WeChat, Feishu, DingTalk, Yuque)
- Sandbox commands run as root since v0.8.2
- Maintainers advise against exposing it to the public internet
- Desktop app has no published installer; hardware requirements live in external docs
- no GPU
- Docker + Compose
- Needs Neo4j (optional profile), MinIO (optional profile), Langfuse (optional profile)
- Models: OpenAI, DeepSeek, Qwen, Zhipu, Hunyuan, Gemini, MiniMax, NVIDIA, LiteLLM, Ollama
- port 80