# LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning arXiv: https://arxiv.org/abs/2605.28120 ยท HTML: https://mlsanity.com/paper/2605.28120/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. - Builds on 38 of its 76 references that are in the map. - First posted on arXiv 27 May 2026. ## Placed in - LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/) - Retrieval-augmented generation / [Legal and financial RAG](https://mlsanity.com/rag/legal-and-financial-rag/) - LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/) ## Builds on (most cited first, 30 of 38) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024) - [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024) - [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024) - [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024) - [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024) - [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024) - [From RAG to Memory: Non-Parametric Continual Learning for Large Language Models](https://mlsanity.com/paper/2502.14802/) (2025) - [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023) - [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018) - [SaulLM-7B: A pioneering Large Language Model for Law](https://mlsanity.com/paper/2403.03883/) (2024) - [Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture](https://mlsanity.com/paper/2306.16092/) (2023) - [DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services](https://mlsanity.com/paper/2309.11325/) (2023) - [LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain](https://mlsanity.com/paper/2408.10343/) (2024) - [A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems](https://mlsanity.com/paper/2504.09037/) (2025) - [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025) - [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020) - [Legal Prompting: Teaching a Language Model to Think Like a Lawyer](https://mlsanity.com/paper/2212.01326/) (2022) - [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023) - [When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.05690/) (2025) - [LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model](https://mlsanity.com/paper/2406.04614/) (2024) - [Exploring the Use of Text Classification in the Legal Domain](https://mlsanity.com/paper/1710.09306/) (2017) - [Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2602.05665/) (2026) - [Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction](https://mlsanity.com/paper/2407.01964/) (2024) - [Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation](https://mlsanity.com/paper/2406.19760/) (2024) - [LegalDelta: Enhancing Legal Reasoning in LLMs via Reinforcement Learning with Chain-of-Thought Guided Information Gain](https://mlsanity.com/paper/2508.12281/) (2025) - [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025) ## Built on by (most cited first, 5 of 5) - [MemSyco-Bench: Benchmarking Sycophancy in Agent Memory](https://mlsanity.com/paper/2607.01071/) (2026) - [HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document](https://mlsanity.com/paper/2607.24861/) (2026) - [LEGO: Synergizing Expert GraphRAG and Expert Chain-of-Thought for Legal Reasoning](https://mlsanity.com/paper/2609.27009/) (2026) - [LabourCrew: A Multi-Agent RAG Framework for Trustworthy Adversarial Deliberation and Statutory Reasoning over Labour Law](https://mlsanity.com/paper/2609.27814/) (2026) - [ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints](https://mlsanity.com/paper/2609.30184/) (2026) ## Abstract (arXiv) Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more coherent and effective reasoning. However, applying it to specific domains like legal reasoning faces critical challenges. (i) Legal corpora are heterogeneous, containing multi-granular knowledge from cases, articles and interpretations. A flat knowledge graph cannot adequately differentiate between factual details, applied rules, and abstract principles, limiting accurate retrieval. (ii) Reliable legal judgment demands transparent, evidence-based reasoning. Traditional RAG passes retrieved context directly to an LLM without verification, resulting in opaque, error-prone reasoning. To this end, we propose LegalGraphRAG, a framework designed for reliable legal reasoning. Our approach introduces two core components: a hierarchical legal graph that hierarchically organizes legal sources to enable retrieval at appropriate abstraction levels, and a multi-agent system for reliable legal reasoning, where a Researcher retrieves candidate evidence, an Auditor rigorously verifies its validity against source documents, and an Adjudicator synthesizes the set of verified evidence to render a final judgment. Extensive experiments show that LegalGraphRAG achieves the state-of-the-art performance, outperforming existing GraphRAG baselines in accurate and trustworthy legal analysis. Our code, datasets and implementation details are available at https://github.com/XMUDeepLIT/LegalGraphRAG.