A Multi-Agent Retrieval-Augmented Generation Framework for Context-Aware Legal Document Analysis

Abstract: Legal document analysis requires high accuracy, traceability, and semantic understanding. While large language models (LLMs) provide strong generative capabilities, they suffer from hallucinations and lack of grounding in authoritative sources. This paper presents a Multi-Agent Retrieval-Augmented Generation (RAG) framework for legal document analysis. The system integrates semantic retrieval, vector embeddings, and collaborative agent-based reasoning to produce context-aware legal responses. A modular architecture consisting of retrieval, summarization, precedent discovery, and fact-checking agents aims to improve reliability and explainability. The framework is designed for scalable enterprise deployment and evaluated using grounding-based and qualitative evaluation metrics.

Keywords: Retrieval-Augmented Generation, Legal NLP, Multi-Agent Systems, Vector Databases