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arXiv · 2601.06216

LLM Agents in Law: Taxonomy, Applications, and Challenges

Shuang Liu·Ruijia Zhang·Ruoyun Ma·Yujia Deng·Lanyi Zhu·Jiayu Li·Zelong Li·Zhibin Shen·Mengnan Du
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Abstract

Large language models (LLMs) have precipitated a dramatic improvement in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability. Recently, LLM agents have attracted significant attention as a solution to these challenges, utilizing advanced capabilities such as planning, memory, and tool usage to meet the rigorous standards of legal practice. In this paper, we present a comprehensive survey of LLM agents for legal tasks, analyzing how these architectures bridge the gap between technical capabilities and domain-specific needs. Our major contributions include: (1) systematically analyzing the technical transition from standard legal LLMs to legal agents; (2) presenting a structured taxonomy of current agent applications across distinct legal practice areas; (3) discussing evaluation methodologies specifically for agentic performance in law; and (4) identifying open challenges and outlining future directions for developing robust and autonomous legal assistants.

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Shuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng, Lanyi Zhu, Jiayu Li, Zelong Li, Zhibin Shen, Mengnan Du. 2026-01-08. LLM Agents in Law: Taxonomy, Applications, and Challenges. https://arxiv.org/abs/2601.06216

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