RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent c...
Copy the install, test the workflow, then decide if it earns a permanent slot.
Fresh repo activity plus visible builder pull. This is the kind of tool people test before it turns obvious.
Copy the install, test the workflow, then decide if it earns a permanent slot.
Reasonable to try, but it will take more than a quick skim to get real signal.
GitHub health 62/100. no security policy. 2,099 open issues make this testable, but not something to trust blind.
AI Agent
Universal
Model
Multiple
Fastest way to find out if ragflow belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
git clone https://github.com/infiniflow/ragflow ~/.claude/agents/ragflowRun this first. You will know quickly if the workflow earns a permanent slot.
rm -rf ~/.claude/agents/ragflowNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude/ ├─ commands/ ├─ agents/ │ └─ ragflow/ ← installs here └─ settings.json
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. An open-source agent for the AI coding ecosystem.
Source: GitHub repository
Source check: July 23, 2026
Upstream commit: July 23, 2026
Repository state: Not marked archived
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