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in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability.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.
@article{arxiv.2601.06216,
title = {LLM Agents in Law: Taxonomy, Applications, and Challenges},
author = {Shuang Liu and Ruijia Zhang and Ruoyun Ma and Yujia Deng and Lanyi Zhu and Jiayu Li and Zelong Li and Zhibin Shen and Mengnan Du},
journal= {arXiv preprint arXiv:2601.06216},
year = {2026}
}