# E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness

arXiv: https://arxiv.org/abs/2505.24226 · HTML: https://mlsanity.com/paper/2505.24226/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- First posted on arXiv 30 May 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Built on by (most cited first, 7 of 7)

- [Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning](https://mlsanity.com/paper/2508.19855/) (2025)
- [Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval](https://mlsanity.com/paper/2509.21710/) (2025)
- [Agents-K1: Towards Agent-native Knowledge Orchestration](https://mlsanity.com/paper/2606.13669/) (2026)
- [SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory](https://mlsanity.com/paper/2605.12061/) (2026)
- [HyperSU: Corpus-Driven Semantic-Unit Hypergraph for Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.28351/) (2026)
- [IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval](https://mlsanity.com/paper/2606.06044/) (2026)
- [WFM: Wiki Foundation Model for Complex Agentic Reasoning](https://mlsanity.com/paper/2609.18182/) (2026)

## Abstract (arXiv)

Graph-based RAG methods like GraphRAG have shown promising global understanding of the knowledge base by constructing hierarchical entity graphs. However, they often suffer from inefficiency and rely on manually pre-defined query modes, limiting practical use. In this paper, we propose E^2GraphRAG, a streamlined graph-based RAG framework that improves both Efficiency and Effectiveness. During the indexing stage, E^2GraphRAG constructs a summary tree with large language models and an entity graph with SpaCy based on document chunks. We then construct bidirectional indexes between entities and chunks to capture their many-to-many relationships, enabling fast lookup during both local and global retrieval. For the retrieval stage, we design an adaptive retrieval strategy that leverages the graph structure to retrieve and select between local and global modes. Experiments show that E^2GraphRAG achieves up to 10 times faster indexing than GraphRAG and 100 times speedup over LightRAG in retrieval while maintaining competitive QA performance.