by Ayanami0730 · Agent Tool · ★ 187
A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces If you find our project helpful, please give us a star ⭐ on GitHub! 🚀 Quick Start bash Install git clone https://github.com/Ayanami0730/arag.git && cd arag uv sync --extra full # or: pip install -e ".[full]" Download benchmark datasets from HuggingFace git clone https://huggingface.co/datasets/Ayanami0730/ragtest data --depth 1 rm -rf data/.git data/README.md Build embedding index We use Qwen3-Embedding-0.6B in our paper (https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) You can also use a local path:...
| Stars | 187 |
| Forks | 23 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 62.8582548304153/100 |
| Open Issues | 2 |
| Last Updated | 2026-02-06 |
| Created | 2026-02-03 |
| Platforms | python |
| Est. Tokens | ~172k |
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arag is A-RAG: Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces. State-of-the-art RAG framework with keyword, semantic, and chunk read tools for multi-hop QA.. It is categorized as a Agent Tool with 187 GitHub stars.
arag is primarily written in Python. It covers topics such as agent, agentic-ai, agenticrag.
You can find installation instructions and usage details in the arag GitHub repository at github.com/Ayanami0730/arag. The project has 187 stars and 23 forks, indicating an active community.