RAGFlow vs WeKnora
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
WeKnora
Enterprise knowledge base combining RAG Q&A, agents and generated wikis
| What we compare | RAGFlow | WeKnora |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 95, widely used | 69, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 94, healthy | 89, healthy |
| Easy to run | 33, some setup | 50, easy |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 92k | 33.1k |
| License | Apache-2.0 (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Sep 2026 |
| Language | Go | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
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
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