LightRAG vs RAGFlow
On the evidence we track, LightRAG leads this comparison with a composite score of 62/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for rag tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
LightRAG
LeaderAn open-source RAG system designed for high scalability that uses both knowledge graphs and vector search to retrieve information from large datasets with reduced computational overhead and faster response times.
RAGFlow
RAGFlow is an open-source RAG engine that streamlines data processing and integration for building production-ready AI systems. It supports multiple data formats and provides configurable LLM and embedding options with agent-based workflows.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
LightRAG
Leader- PostgreSQL
- Neo4j
- MongoDB
- Milvus
- Qdrant
- Redis
- Memgraph
- MinerU
- Docling
RAGFlow
- Discord
- Feishu
- Telegram
- Line
- Confluence
- S3
- Notion
- Google Drive
- OpenAI
- DeepSeek
- Gemini
- OpenClaw
- OpenAI GPT-5
- Google Gemini 3 Pro
- DeepSeek v4
- MCP
LightRAG or RAGFlow: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.