A Multi-Agent AI Framework for Legal Aid: Real-World Interactive Dialogue in Case Consultations

This paper presents a multi-agent AI framework for legal aid, designed to support real-world case fact management through interactive dialogue. Our agent framework emulates the iterative questioning, clarification, and synthesis processes of legal professionals, not solely on the fragmented information initially provided by litigants. By engaging in multi-turn interactions, the system incrementally supplements missing details and mitigates risks of misinterpretation, thereby aligning more closely with the dynamics of real legal consultations. A key contribution of this work is the use of real-world legal aid case records as training and evaluation material, ensuring that the framework is grounded in authentic data rather than synthetic simulations. The system is implemented as a collaboration among specialized agents: (1) litigantTwins, which maintains factual integrity and guards against hallucinations; (2) legalAider, which leverages legal knowledge to generate context-sensitive follow-up questions and update case narratives; and (3) Evaluator, which compares AI-generated case records against ground truth facts, assessing factual correctness through qualitative and quantitative measures. Technically, the framework can be built on LLMs, with multi-agent system and prompt design enabling robust coordination. This architecture enhances the quality of fact construction and reasoning, while also offering a scalable solution for online public legal aid services. Beyond its technical contributions, this research highlights its public value, accessibility, and commitment to fairness in the distribution of legal resources. By integrating multi-agent AI with real-world case data, the framework addresses both the technological and socio-legal dimensions of legal information systems, advancing on legal knowledge management, deployment of conversational agents, and normative reasoning in multi-agent systems.