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in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability.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.

翻译：大型语言模型（LLM）在法律领域引发了显著进步，但独立模型的部署仍面临幻觉、信息过时和可验证性等方面的重大局限。近年来，LLM智能体作为应对这些挑战的解决方案受到广泛关注，其通过规划、记忆和工具使用等高级能力来满足法律实践的严谨要求。本文对面向法律任务的LLM智能体进行了全面综述，分析了这些架构如何弥合技术能力与领域特定需求之间的差距。我们的主要贡献包括：（1）系统分析从标准法律LLM到法律智能体的技术演进路径；（2）提出跨不同法律实践领域的现行智能体应用结构化分类体系；（3）探讨专门针对法律领域智能体性能的评估方法；（4）指出开放挑战并展望构建鲁棒自主法律助手的未来发展方向。