LLM Agents in Law: Taxonomy, Applications, and Challenges
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
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in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability.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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 80 of its 147 references that are in the map. First posted on arXiv 8 Jan 2026.
Builds on
80 of 147 references.
Closest ancestors · 26
Same direction, newest first
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Other references · 54
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50 most cited of 54 All 54 (JSON)
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Citing papers per year: 2026: 5.
5 papers.
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