@inproceedings{chen-etal-2026-graphrag,
title = "Is {G}raph{RAG} Needed? From Basic {RAG} to Graph-/Agentic Solutions with Context Optimization",
author = "Chen, Long and
Razkenari, Ryan and
Zhou, Yuxuan and
Tian, Yuan and
Ghosh, Rahul and
Pappakrishnan, Venkatesh and
Ahuja, Disha and
Ravipati, Vidya Sagar",
editor = "Mille, Simon and
Gehrmann, Sebastian and
Schmidtov{\'a}, Patr{\'i}cia and
Du{\v{s}}ek, Ond{\v{r}}ej and
Fadaee, Marzieh and
Lo, Kyle and
Santus, Enrico and
Stanovsky, Gabriel",
booktitle = "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics ({GEM})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.gem-main.40/",
pages = "428--442",
ISBN = "979-8-89176-423-1",
abstract = "As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison. These scenarios are designed for real use cases regarding data and domain restrictions, spanning from simple document-based retrieval to advanced features such as hybrid text-graph retrieval, integration with computed or pre-defined domain knowledge graphs, agentic multi-step planning, and agent-graph integration. Besides, we present a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues, efficiently managing text and graph retrievals with new representations and agentic loop design, leading to 19{\%}-53{\%} reduction on token usage. Moreover, further analysis identifies a retrieval-generation gap where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate advanced retrieval benefits. This work provides data-driven insights on when and how to use them for building production-ready intelligent RAG systems."
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%0 Conference Proceedings
%T Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization
%A Chen, Long
%A Razkenari, Ryan
%A Zhou, Yuxuan
%A Tian, Yuan
%A Ghosh, Rahul
%A Pappakrishnan, Venkatesh
%A Ahuja, Disha
%A Ravipati, Vidya Sagar
%Y Mille, Simon
%Y Gehrmann, Sebastian
%Y Schmidtová, Patrícia
%Y Dušek, Ondřej
%Y Fadaee, Marzieh
%Y Lo, Kyle
%Y Santus, Enrico
%Y Stanovsky, Gabriel
%S Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-423-1
%F chen-etal-2026-graphrag
%X As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison. These scenarios are designed for real use cases regarding data and domain restrictions, spanning from simple document-based retrieval to advanced features such as hybrid text-graph retrieval, integration with computed or pre-defined domain knowledge graphs, agentic multi-step planning, and agent-graph integration. Besides, we present a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues, efficiently managing text and graph retrievals with new representations and agentic loop design, leading to 19%-53% reduction on token usage. Moreover, further analysis identifies a retrieval-generation gap where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate advanced retrieval benefits. This work provides data-driven insights on when and how to use them for building production-ready intelligent RAG systems.
%U https://aclanthology.org/2026.gem-main.40/
%P 428-442
Markdown (Informal)
[Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization](https://aclanthology.org/2026.gem-main.40/) (Chen et al., GEM 2026)
ACL
- Long Chen, Ryan Razkenari, Yuxuan Zhou, Yuan Tian, Rahul Ghosh, Venkatesh Pappakrishnan, Disha Ahuja, and Vidya Sagar Ravipati. 2026. Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization. In Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM), pages 428–442, San Diego, California, USA. Association for Computational Linguistics.