ragflow
RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.
Concise Brief · Core Value & Positioning
Jargon-free breakdown of core value and deliverable form
Agent-native RAG engine with deep document parsing and knowledge compilation, serving as context infrastructure for AI applications.
Systems & Services
AI Agent / LLM Engineering
Go · Apache-2.0
Next star milestone
100,000 ★
93,397 / 100,000 · 93%
7-day net growth +448
Star history
Tracking 103 verified historical snapshots
Maintenance & community signals
- Last push
- 2026-11-03
- Fork / Star
- 12.0%
- 7-day net stars
- +448
Public metadata does not certify product quality, code safety or authentic stars. Value assessment requires separate evidence.
Geek Clone & Cloud Code Inspector
1-click clone or inspect source code & AI architecture online
Cloning downloads the source; follow the project README for dependencies and installation.
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GitValue · Value evidence
Unscored · Evidence coverage 0%
Provisional rubric; documentation and runtime testing are separate. Missing evidence does not imply low value.
Frequently Asked Questions about ragflow
What is infiniflow/ragflow (ragflow) and what problem does it solve?
Agent-native RAG engine with deep document parsing and knowledge compilation, serving as context infrastructure for AI applications. Maintained by infiniflow and primarily written in Go, it is ranked in the Systems & Services category on GitRank.
How fast is ragflow growing on GitHub?
infiniflow/ragflow has 93,397 stars and 11,210 forks in the latest snapshot, with +448 net stars over seven days. These are community and activity signals rather than proof of project quality.
Which related projects can I compare with ragflow?
Related-use or topic candidates include langgenius/dify, Mintplex-Labs/anything-llm, pathwaycom/serviette, run-llama/llama_index. Related does not mean feature-equivalent; check official documentation and licenses.
How do I install or clone ragflow?
You can clone the repository directly via `git clone https://github.com/infiniflow/ragflow.git` and follow the official README instructions on GitHub.