GraphContainer: A Unified Platform for Comparing and Debugging Graph RAG Methods

Abstract:Graph RAG mitigates hallucinations and stale knowledge in LLMs, particularly for multi-hop question answering. However, existing approaches remain highly fragmented and incompatible. The structural heterogeneity of graph formats across different frameworks and the lack of granular visualization tools make it exceedingly difficult to evaluate and compare retrieval behaviors. To bridge this gap, we propose GraphContainer, a novel platform designed to unify and visualize diverse graph RAG workflows. GraphContainer features two key components: (1) a Unified Graph Representation (UGR) layer that seamlessly standardizes multi-format graphs, and (2) a Graph Recorder that tracks and visually renders the step-by-step retrieval process. Through an interactive web interface, we demonstrate GraphContainer's ability to import heterogeneous graphs and perform live, traceable visual debugging of graph RAG methods. Ultimately, we show how GraphContainer enables controlled comparisons of various graph formats and retrieval strategies, lowering the barrier for researchers and practitioners to design optimal graph RAG pipelines. A demonstration video is available at this https URL.

Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2607.19362 [cs.AI]

(or arXiv:2607.19362v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.19362 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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