We open-sourced a graph-free multi-hop RAG framework — matches Graph-RAG accuracy without the rebuild cost (Apache-2.0)

Reddit r/AI_Agents 07/02/26, 05:14 AM Tools

open-source rag multi-hop framework dense-index query-time-orchestration

Summary

MOTHRAG is a graph-free multi-hop RAG framework that matches the accuracy of graph-based systems like GraphRAG and HippoRAG on benchmarks, while avoiding costly graph rebuilds by using a dense index and query-time orchestration.

We just open-sourced MOTHRAG - a multi-hop RAG framework that skips the knowledge graph entirely. The problem we kept running into: the accurate multi-hop systems (GraphRAG, HippoRAG, RAPTOR) all build a graph offline, and every time the data changes you rebuild it. For a corpus that updates often, that's a constant re-indexing bill. MOTHRAG uses a graph-free dense index with query-time orchestration instead, no graph, no GPU, every component behind a commodity API. On multi-hop benchmarks it matches the graph-based systems, and updates are just embed-and-append instead of a full rebuild. Benchmark MOTHRAG (ours) GraphRAG HippoRAG RAPTOR HotpotQA 78.1 68.6 75.5 69.5 2WikiMultiHop 76.3 58.6 71.0 52.1 MuSiQue 50.5 38.5 48.6 28.9 Apache-2.0, pip install + API keys to run. Honest weak spot that we have right now: recall bottlenecks on MuSiQue, still working on that one tho. Repo in the comments. Would love feedback from anyone running RAG on changing data in production!

Original Article

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