RAGSearch benchmarks agentic dense RAG against GraphRAG
RAGSearch is an open-source benchmark and codebase for comparing dense RAG and GraphRAG pipelines under agentic search. It standardizes retrieval budgets, backbone choice, and inference protocols so teams can compare accuracy, preprocessing cost, online efficiency, and stability across training-free and RL-based setups. The main takeaway is that agentic search narrows the gap to GraphRAG significantly, but GraphRAG still keeps an advantage on harder multi-hop reasoning when its offline indexing cost is justified.
Hot take: this is less a verdict that GraphRAG is redundant and more evidence that better search orchestration can recover a lot of graph-like performance without the full indexing overhead.
- –The benchmark framing is the point: fixed budgets and full-test evaluation make the comparison much more credible than ad hoc demos.
- –Dense RAG becomes meaningfully stronger once agentic multi-round retrieval is allowed, especially in RL-based settings.
- –GraphRAG still looks best where explicit structure matters most, especially for complex multi-hop questions and more stable behavior.
- –The useful contribution is operational, not just academic: it compares not only accuracy but also preprocessing cost and online efficiency.
DISCOVERED
45d ago
2026-04-16
PUBLISHED
45d ago
2026-04-16
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